Global variability in belowground autotrophic respiration in terrestrial ecosystems
<p>Belowground autotrophic respiration (RA) is one of the largest but most highly uncertain carbon flux components in terrestrial ecosystems. However, RA has not been explored globally before and still acts as a “black box” in global carbon cycling currently. Such progress and uncertainty moti...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2019-11-01
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| Series: | Earth System Science Data |
| Online Access: | https://www.earth-syst-sci-data.net/11/1839/2019/essd-11-1839-2019.pdf |
| _version_ | 1818152857366429696 |
|---|---|
| author | X. Tang X. Tang S. Fan W. Zhang W. Zhang S. Gao G. Chen L. Shi |
| author_facet | X. Tang X. Tang S. Fan W. Zhang W. Zhang S. Gao G. Chen L. Shi |
| author_sort | X. Tang |
| collection | DOAJ |
| description | <p>Belowground autotrophic respiration (RA) is one of the largest but most highly
uncertain carbon flux components in terrestrial ecosystems. However, RA has
not been explored globally before and still acts as a “black box” in
global carbon cycling currently. Such progress and uncertainty motivate the
development of a global RA dataset and understanding its spatial and temporal
patterns, causes, and responses to future climate change. We applied the random
forest (RF) algorithm to upscale an updated dataset from the Global Soil
Respiration Database (v4) – covering all major ecosystem types and climate
zones with 449 field observations, using globally gridded temperature,
precipitation, soil and other environmental variables. We used a 10-fold
cross validation to evaluate the performance of RF in predicting the spatial
and temporal pattern of RA. Finally, a globally gridded RA dataset from 1980
to 2012 was produced with a spatial resolution of 0.5<span class="inline-formula"><sup>∘</sup></span> <span class="inline-formula">×</span> 0.5<span class="inline-formula"><sup>∘</sup></span> (longitude <span class="inline-formula">×</span> latitude) and a temporal resolution of 1 year (expressed in g C m<span class="inline-formula"><sup>−2</sup></span> yr<span class="inline-formula"><sup>−1</sup></span>; grams of carbon per square meter per year).</p>
<p>Globally, mean RA was <span class="inline-formula">43.8±0.4</span> Pg C yr<span class="inline-formula"><sup>−1</sup></span>, with a temporally
increasing trend of <span class="inline-formula">0.025±0.006</span> Pg C yr<span class="inline-formula"><sup>−2</sup></span> from 1980 to 2012.
Such an incremental trend was widespread, representing 58 % of global land. For
each 1 <span class="inline-formula"><sup>∘</sup></span>C increase in annual mean temperature, global RA increased by <span class="inline-formula">0.85±0.13</span> Pg C yr<span class="inline-formula"><sup>−2</sup></span>, and it was <span class="inline-formula">0.17±0.03</span> Pg C yr<span class="inline-formula"><sup>−2</sup></span>
for a 10 mm increase in annual mean precipitation, indicating positive
feedback of RA to future climate change. Precipitation was the main dominant
climatic driver controlling RA, accounting for 56 % of global land,
and was the most widely spread globally, particularly in dry or semi-arid areas, followed
by shortwave radiation (25 %) and temperature (19 %). Different temporal
patterns for varying climate zones and biomes indicated uneven responses of
RA to future climate change, challenging the perspective that the parameters
of global carbon stimulation are independent of climate zones and biomes. The
developed RA dataset, the missing carbon flux component that is not
constrained and validated in terrestrial ecosystem models and Earth system
models, will provide insights into understanding mechanisms underlying the
spatial and temporal variability in belowground vegetation carbon dynamics.
The developed RA dataset also has great potential to serve as a benchmark
for future data–model comparisons. The developed RA dataset in a common
NetCDF format is freely available at <a href="https://doi.org/10.6084/m9.figshare.7636193">https://doi.org/10.6084/m9.figshare.7636193</a> (Tang et al., 2019).</p> |
| first_indexed | 2024-12-11T14:01:23Z |
| format | Article |
| id | doaj.art-4ed96d3fe9244b8399f2b1d05880075f |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-12-11T14:01:23Z |
| publishDate | 2019-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj.art-4ed96d3fe9244b8399f2b1d05880075f2022-12-22T01:03:50ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162019-11-01111839185210.5194/essd-11-1839-2019Global variability in belowground autotrophic respiration in terrestrial ecosystemsX. Tang0X. Tang1S. Fan2W. Zhang3W. Zhang4S. Gao5G. Chen6L. Shi7College of Earth Science, Chengdu University of Technology, Chengdu, ChinaState Environmental Protection Key Laboratory of Synergetic Control and Joint Remediation for Soil & Water Pollution, Chengdu University of Technology, Chengdu, ChinaKey Laboratory of Bamboo and Rattan, International Centre for Bamboo and Rattan, Beijing, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing, ChinaSchool of Life Sciences, University of Technology Sydney, Sydney, New South Wales, AustraliaSchool of Life Sciences, University of Technology Sydney, Sydney, New South Wales, AustraliaCollege of Earth Science, Chengdu University of Technology, Chengdu, ChinaKey Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, College of Environment and Planning, Henan University, Jinming Avenue, Kaifeng, China<p>Belowground autotrophic respiration (RA) is one of the largest but most highly uncertain carbon flux components in terrestrial ecosystems. However, RA has not been explored globally before and still acts as a “black box” in global carbon cycling currently. Such progress and uncertainty motivate the development of a global RA dataset and understanding its spatial and temporal patterns, causes, and responses to future climate change. We applied the random forest (RF) algorithm to upscale an updated dataset from the Global Soil Respiration Database (v4) – covering all major ecosystem types and climate zones with 449 field observations, using globally gridded temperature, precipitation, soil and other environmental variables. We used a 10-fold cross validation to evaluate the performance of RF in predicting the spatial and temporal pattern of RA. Finally, a globally gridded RA dataset from 1980 to 2012 was produced with a spatial resolution of 0.5<span class="inline-formula"><sup>∘</sup></span> <span class="inline-formula">×</span> 0.5<span class="inline-formula"><sup>∘</sup></span> (longitude <span class="inline-formula">×</span> latitude) and a temporal resolution of 1 year (expressed in g C m<span class="inline-formula"><sup>−2</sup></span> yr<span class="inline-formula"><sup>−1</sup></span>; grams of carbon per square meter per year).</p> <p>Globally, mean RA was <span class="inline-formula">43.8±0.4</span> Pg C yr<span class="inline-formula"><sup>−1</sup></span>, with a temporally increasing trend of <span class="inline-formula">0.025±0.006</span> Pg C yr<span class="inline-formula"><sup>−2</sup></span> from 1980 to 2012. Such an incremental trend was widespread, representing 58 % of global land. For each 1 <span class="inline-formula"><sup>∘</sup></span>C increase in annual mean temperature, global RA increased by <span class="inline-formula">0.85±0.13</span> Pg C yr<span class="inline-formula"><sup>−2</sup></span>, and it was <span class="inline-formula">0.17±0.03</span> Pg C yr<span class="inline-formula"><sup>−2</sup></span> for a 10 mm increase in annual mean precipitation, indicating positive feedback of RA to future climate change. Precipitation was the main dominant climatic driver controlling RA, accounting for 56 % of global land, and was the most widely spread globally, particularly in dry or semi-arid areas, followed by shortwave radiation (25 %) and temperature (19 %). Different temporal patterns for varying climate zones and biomes indicated uneven responses of RA to future climate change, challenging the perspective that the parameters of global carbon stimulation are independent of climate zones and biomes. The developed RA dataset, the missing carbon flux component that is not constrained and validated in terrestrial ecosystem models and Earth system models, will provide insights into understanding mechanisms underlying the spatial and temporal variability in belowground vegetation carbon dynamics. The developed RA dataset also has great potential to serve as a benchmark for future data–model comparisons. The developed RA dataset in a common NetCDF format is freely available at <a href="https://doi.org/10.6084/m9.figshare.7636193">https://doi.org/10.6084/m9.figshare.7636193</a> (Tang et al., 2019).</p>https://www.earth-syst-sci-data.net/11/1839/2019/essd-11-1839-2019.pdf |
| spellingShingle | X. Tang X. Tang S. Fan W. Zhang W. Zhang S. Gao G. Chen L. Shi Global variability in belowground autotrophic respiration in terrestrial ecosystems Earth System Science Data |
| title | Global variability in belowground autotrophic respiration in terrestrial ecosystems |
| title_full | Global variability in belowground autotrophic respiration in terrestrial ecosystems |
| title_fullStr | Global variability in belowground autotrophic respiration in terrestrial ecosystems |
| title_full_unstemmed | Global variability in belowground autotrophic respiration in terrestrial ecosystems |
| title_short | Global variability in belowground autotrophic respiration in terrestrial ecosystems |
| title_sort | global variability in belowground autotrophic respiration in terrestrial ecosystems |
| url | https://www.earth-syst-sci-data.net/11/1839/2019/essd-11-1839-2019.pdf |
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