Boreal Vegetation and Soil Carbon Dynamics in Response to a Changing Climate and Soil Variables

2021 ◽  
Author(s):  
John Galbraith ◽  
Pavel Krasilnikov ◽  
Cornelia Rumpel
Keyword(s):  
Boreal Forest ◽  
Vegetation Change ◽  
Precipitation Amount ◽  
Carbon Dynamics ◽  
The Arctic ◽  
Higher Plant ◽  
Rock Fragments ◽  
Changing Climate ◽  
Soil Variables ◽  
Air Temperatures

<p>Many soils in the Boreal forest regions of the Arctic store very large amounts of carbon in the active layer above permafrost, and store significant amounts of carbon within the permafrost. Soils that are well drained, high in rock fragments, shallow to rock or rubble, or covered with ice are exceptions. No other region on Earth stores more carbon on average than the Arctic regions, especially in wetlands. However, changes in vegetation and soil are expected under warming climates. Research questions have arisen about future changes in vegetation and net carbon flux as soil and air temperatures climb, as precipitation amount and type changes, and as the growing season lengthens. A review of recent literature will be conducted to look at effects of vegetation change and annual carbon dynamics in Boreal forest and wetland soils under warming climates. Environmental variables such as soil temperature, hydrology, microbial and higher plant growth, digestibility of young and old carbon, fire, location zone, extent and type of permafrost thaw slow vs sudden collapse), and N and P nutrient balances will affect carbon stocks in addition to changing climate.</p>

scholarly journals Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

2021 ◽  
Vol 13 (12) ◽  
pp. 2283
Author(s):  
Hyangsun Han ◽  
Sungjae Lee ◽  
Hyun-Cheol Kim ◽  
Miae Kim
Keyword(s):  
Random Forest ◽  
Sea Ice ◽  
Arctic Ocean ◽  
Open Water ◽  
The Arctic ◽  
Atmospheric Effects ◽  
Sea Ice Concentration ◽  
Air Temperatures ◽  
The Pacific ◽  
Ice Concentration

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

Comparison of the Arctic upper-air temperatures from radiosonde and radio occultation observations

2018 ◽  
Vol 37 (1) ◽  
pp. 30-39
Author(s):  
Liang Chang ◽  
Lixin Guo ◽  
Guiping Feng ◽  
Xuerui Wu ◽  
Guoping Gao ◽  
...  
Keyword(s):  
Radio Occultation ◽  
The Arctic ◽  
Air Temperatures

scholarly journals Satellite observations of changes in snow-covered land surface albedo during spring in the Northern Hemisphere

2015 ◽  
Vol 9 (5) ◽  
pp. 1879-1893 ◽  
Author(s):  
K. Atlaskina ◽  
F. Berninger ◽  
G. de Leeuw
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Precipitation Amount ◽  
Snow Cover Fraction ◽  
Air Temperatures ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Thirteen years of Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo data for the Northern Hemisphere during the spring months (March–May) were analyzed to determine temporal and spatial changes over snow-covered land surfaces. Tendencies in land surface albedo change north of 50° N were analyzed using data on snow cover fraction, air temperature, vegetation index and precipitation. To this end, the study domain was divided into six smaller areas, based on their geographical position and climate similarity. Strong differences were observed between these areas. As expected, snow cover fraction (SCF) has a strong influence on the albedo in the study area and can explain 56 % of variation of albedo in March, 76 % in April and 92 % in May. Therefore the effects of other parameters were investigated only for areas with 100 % SCF. The second largest driver for snow-covered land surface albedo changes is the air temperature when it exceeds a value between −15 and −10 °C, depending on the region. At monthly mean air temperatures below this value no albedo changes are observed. The Enhanced Vegetation Index (EVI) and precipitation amount and frequency were independently examined as possible candidates to explain observed changes in albedo for areas with 100 % SCF. Amount and frequency of precipitation were identified to influence the albedo over some areas in Eurasia and North America, but no clear effects were observed in other areas. EVI is positively correlated with albedo in Chukotka Peninsula and negatively in eastern Siberia. For other regions the spatial variability of the correlation fields is too high to reach any conclusions.

scholarly journals Extreme Warming in the Kara Sea and Barents Sea during the Winter Period 2000–16

2017 ◽  
Vol 30 (22) ◽  
pp. 8913-8927 ◽  
Author(s):  
Svenja H. E. Kohnemann ◽  
Günther Heinemann ◽  
David H. Bromwich ◽  
Oliver Gutjahr
Keyword(s):  
Sea Ice ◽  
Air Temperature ◽  
Barents Sea ◽  
Kara Sea ◽  
The Arctic ◽  
Winter Period ◽  
Limited Area ◽  
Spatial And Temporal Variability ◽  
Large Variability ◽  
Air Temperatures

The regional climate model COSMO in Climate Limited-Area Mode (COSMO-CLM or CCLM) is used with a high resolution of 15 km for the entire Arctic for all winters 2002/03–2014/15. The simulations show a high spatial and temporal variability of the recent 2-m air temperature increase in the Arctic. The maximum warming occurs north of Novaya Zemlya in the Kara Sea and Barents Sea between March 2003 and 2012 and is responsible for up to a 20°C increase. Land-based observations confirm the increase but do not cover the maximum regions that are located over the ocean and sea ice. Also, the 30-km version of the Arctic System Reanalysis (ASR) is used to verify the CCLM for the overlapping time period 2002/03–2011/12. The differences between CCLM and ASR 2-m air temperatures vary slightly within 1°C for the ocean and sea ice area. Thus, ASR captures the extreme warming as well. The monthly 2-m air temperatures of observations and ERA-Interim data show a large variability for the winters 1979–2016. Nevertheless, the air temperature rise since the beginning of the twenty-first century is up to 8 times higher than in the decades before. The sea ice decrease is identified as the likely reason for the warming. The vertical temperature profiles show that the warming has a maximum near the surface, but a 0.5°C yr−1 increase is found up to 2 km. CCLM, ASR, and also the coarser resolved ERA-Interim data show that February and March are the months with the highest 2-m air temperature increases, averaged over the ocean and sea ice area north of 70°N; for CCLM the warming amounts to an average of almost 5°C for 2002/03–2011/12.

scholarly journals Remote sensing assessment of glacial loss and vegetation change from 1989 to 2016: Pond Inlet, Nunavut

2021 ◽  
Author(s):  
Ruby R. Pennell
Keyword(s):  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Relevant Literature ◽  
Arctic Tundra ◽  
The Arctic ◽  
Landscape Changes ◽  
Storm Events ◽  
Time Period ◽  
Induced Changes

The climate change phenomenon occurring across the globe is having an increasingly alarming effect on Canada’s Arctic. Warming temperatures can have wide spanning impacts ranging from more rain and storm events, to increasing runoff, thawing permafrost, sea ice decline, melting glaciers, ecosystem disruption, and more. The purpose of this MRP was to assess the climate-induced landscape changes, including glacial loss and vegetation change, in Pond Inlet, Nunavut. A time series analysis was performed using the intervals 1989-1997, 1997-2005, and 2005-2016. The two methods for monitoring change were 1) the Normalized Difference Snow Index (NDSI) to detect glacial change, and 2) the Normalized Difference Vegetation Index (NDVI) to detect vegetation change, both utilizing threshold and masking techniques to increase accuracy. It was found that the percent of glacial loss and vegetation change in Pond Inlet had consistently increased throughout each time period. The area of glacial loss grew through each period to a maximum of 376 km2 of glacial loss in the last decade. Similarly, the area of the Arctic tundra that experienced vegetation change increased in each time period to a maximum of 660 km2 in the last decade. This vegetation change was characterized by overall increasing values of NDVI, revealing that many sections of the Arctic tundra in Pond Inlet were increasing in biomass. However, case study analysis revealed pixel clustering around the lower vegetation class thresholds used to classify change, indicating that shifts between these vegetation classes were likely exaggerated. Shifts between the higher vegetation classes were significant, and were what contributed to the most change in the last decade. The observations of higher glacial melt and increases in biomass are occurring in parallel with the increasing temperatures in Pond Inlet. Relevant literature in the Arctic agrees with the findings of this MRP that there are significant trends of glacial loss and vegetation greening and many studies attribute this directly to climate warming. The results of this study provide the necessary background with regards to landscape changes which could be used in future field studies investigating the climate induced changes in Pond Inlet. This study also demonstrates that significant landscape modifications have occurred in the recent decades and there is a strong need for continued research and monitoring of climate induced changes.

scholarly journals Polar Amplification and Ice Free Conditions under 1.5, 2 and 3 °C of Global Warming as Simulated by CMIP5 and CMIP6 Models

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1494
Author(s):  
Fernanda Casagrande ◽  
Francisco A. B. Neto ◽  
Ronald B. de Souza ◽  
Paulo Nobre
Keyword(s):  
Global Warming ◽  
Temperature Response ◽  
Surface Air Temperature ◽  
The Arctic ◽  
Polar Regions ◽  
High Latitudes ◽  
Near Surface ◽  
Polar Amplification ◽  
Air Temperatures ◽  
Average Rise

One of the most visible signs of global warming is the fast change in the polar regions. The increase in Arctic temperatures, for instance, is almost twice as large as the global average in recent decades. This phenomenon is known as the Arctic Amplification and reflects several mutually supporting processes. An equivalent albeit less studied phenomenon occurs in Antarctica. Here, we used numerical climate simulations obtained from CMIP5 and CMIP6 to investigate the effects of +1.5, 2 and 3 °C warming thresholds for sea ice changes and polar amplification. Our results show robust patterns of near-surface air-temperature response to global warming at high latitudes. The year in which the average air temperatures brought from CMIP5 and CMIP6 models rises by 1.5 °C is 2024. An average rise of 2 °C (3 °C) global warming occurs in 2042 (2063). The equivalent warming at northern (southern) high latitudes under scenarios of 1.5 °C global warming is about 3 °C (1.8 °C). In scenarios of 3 °C global warming, the equivalent warming in the Arctic (Antarctica) is close to 7 °C (3.5 °C). Ice-free conditions are found in all warming thresholds for both the Arctic and Antarctica, especially from the year 2030 onwards.

scholarly journals Frequency and distribution of winter melt events from passive microwave satellite data in the pan-Arctic, 1988–2013

2016 ◽  
Author(s):  
Libo Wang ◽  
Peter Toose ◽  
Ross Brown ◽  
Chris Derksen
Keyword(s):  
Snow Cover ◽  
Microwave Radiometer ◽  
Surface Air Temperature ◽  
Arctic Region ◽  
Temporal Variations ◽  
Passive Microwave ◽  
The Arctic ◽  
Winter Period ◽  
Strongly Correlated ◽  
Air Temperatures

Abstract. This study presents an algorithm for detecting winter melt events in seasonal snow cover based on temporal variations in the brightness temperature difference between 19 and 37 GHz from satellite passive microwave measurements. An advantage of the passive microwave approach is that it is based on the physical presence of liquid water in the snowpack, which may not be the case with melt events inferred from surface air temperature data. The algorithm is validated using in situ observations from weather stations, snowpit surveys, and a surface-based passive microwave radiometer. The results of running the algorithm over the pan-Arctic region (north of 50º N) for the 1988–2013 period show that winter melt days are relatively rare averaging less than 7 melt days per winter over most areas, with higher numbers of melt days (around two weeks per winter) occurring in more temperate regions of the Arctic (e.g. central Quebec and Labrador, southern Alaska, and Scandinavia). The observed spatial pattern was similar to winter melt events inferred with surface air temperatures from ERA-interim and MERRA reanalysis datasets. There was little evidence of trends in winter melt frequency except decreases over northern Europe attributed to a shortening of the duration of the winter period. The frequency of winter melt events is shown to be strongly correlated to the duration of winter period. This must be taken into account when analyzing trends to avoid generating false increasing trends from shifts in the timing of the snow cover season.

Aerosols and their impacts on future Arctic climate change under different emission projections in the GISS-E2.1 Earth system model

2021 ◽  
Author(s):  
Ulas Im ◽  
Kostas Tsigaridis ◽  
Gregory S. Faluvegi ◽  
Peter L. Langen ◽  
Joshua P. French ◽  
...  
Keyword(s):  
Sea Ice ◽  
Earth System Model ◽  
System Model ◽  
The Arctic ◽  
Earth System ◽  
Anthropogenic Aerosols ◽  
Emission Reductions ◽  
Air Temperatures ◽  
The Future ◽  
Emission Projections

<p>In order to study the future aerosol burdens and their radiative and climate impacts over the Arctic (>60 °N), future (2015-2050) simulations have been carried out using the GISS-E2.1 Earth system model. Different future anthrpogenic emission projections have been used from the Eclipse V6b and the Coupled Model Intercomparison Project Phase 6 (CMIP6) databases. Results showed that Arctic BC, OC and SO<sub>4</sub><sup>2-</sup> burdens decrease significantly in all simulations following the emission projections, with the CMIP6 ensemble showing larger reductions in Arctic aerosol burdens compared to the Eclipse ensemble. For the 2030-2050 period, both the Eclipse Current Legislation (CLE) and the Maximum Feasible Reduction (MFR) ensembles simulated an aerosol top of the atmosphere (TOA) forcing of -0.39±0.01 W m<sup>-2</sup>, of which -0.24±0.01 W m<sup>-2</sup> were attributed to the anthropogenic aerosols. The CMIP6 SSP3-7.0 scenario simulated a TOA aerosol forcing of -0.35 W m<sup>-2</sup> for the same period, while SSP1-2.6 and SSP2-4.5 scenarios simulated a slightly more negative TOA forcing (-0.40 W m<sup>-2</sup>), of which the anthropogenic aerosols accounted for -0.26 W m<sup>-2</sup>. The 2030-2050 mean surface air temperatures are projected to increase by 2.1 °C and 2.4 °C compared to the 1990-2010 mean temperature according to the Eclipse CLE and MFR ensembles, respectively, while the CMIP6 simulation calculated an increase of 1.9 °C (SSP1-2.6) to 2.2 °C (SSP3-7.0). Overall, results show that even the scenarios with largest emission reductions lead to similar impact on the future Arctic surface air temperatures compared to scenarios with smaller emission reductions, while scenarios with no or little mitigation leads to much larger sea-ice loss, implying that even though the magnitude of aerosol reductions lead to similar responses in surface air temperatures, high mitigation of aerosols are still necessary to limit sea-ice loss. </p>

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