Spatial and temporal variations of gross primary production simulated by land surface model BCC_AVIM2.0
Gross primary production (GPP) is the largest flux and a crucial player in the terrestrial carbon cycle and has been studied extensively, yet large uncertainties remain in the spatiotemporal patterns of GPP in both observations and simulations. This study evaluates the performance of the second vers...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-04-01
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| Series: | Advances in Climate Change Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674927823000333 |
| _version_ | 1797816043303862272 |
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| author | Wei-Ping Li Yan-Wu Zhang Mingquan Mu Xue-Li Shi Wen-Yan Zhou Jin-Jun Ji |
| author_facet | Wei-Ping Li Yan-Wu Zhang Mingquan Mu Xue-Li Shi Wen-Yan Zhou Jin-Jun Ji |
| author_sort | Wei-Ping Li |
| collection | DOAJ |
| description | Gross primary production (GPP) is the largest flux and a crucial player in the terrestrial carbon cycle and has been studied extensively, yet large uncertainties remain in the spatiotemporal patterns of GPP in both observations and simulations. This study evaluates the performance of the second version of the Beijing Climate Center Atmosphere−Vegetation Interaction Model (BCC_AVIM2.0) in simulating GPP on multiple spatial and temporal scales in the Coupled Model Intercomparison Project Phase 6 (CMIP6) experiments. Model simulations driven by two meteorological datasets were compared with two observation-based GPP products covering 1982–2008. Spatial patterns of annual GPP show a significant latitudinal gradient in each dataset, increasing from cold (tundra) and dry (desert) biomes to warm (temperate) and humid (tropical rainforest) biomes. BCC_AVIM2.0 overestimates GPP in most parts of the globe, especially in boreal forest regions and Southeast China, while underestimating GPP in subhumid regions in eastern South America and tropical Africa. The four datasets broadly agree on the GPP seasonal cycle, but BCC_AVIM2.0 predicts an earlier beginning of spring growth and a larger amplitude of seasonal variations than those in the observations. The observation-based datasets exhibit slight interannual variability (IAV) and weak GPP linear trends, while the BCC_AVIM2.0 simulations demonstrate relatively large year-to-year variability and significant trends in the low-latitudes and temperate monsoon regions in North America and East Asia. Regarding the possible relationships between annual means of GPP and climate factors, BCC_AVIM2.0 predicts more extensive regions of the globe where the IAV of annual GPP is dominated by precipitation, especially in mid-to-high latitudes of the Northern Hemisphere and tropical Africa, while the observed GPP in the above regions is temperature- or radiation-dominant. The positive GPP biases due to earlier spring growth in boreal forest regions and negative GPP biases in off-equator tropical areas in the BCC_AVIM2.0 simulations imply that cold stress on biomes in boreal mid-to-high latitudes should be strengthened to restrain plant growth, while drought stress in low-latitude regions might be eased to enhance plant production in the future version of BCC_AVIM. |
| first_indexed | 2024-03-13T08:31:50Z |
| format | Article |
| id | doaj.art-52061581c59e49ad8573de8715d89f71 |
| institution | Directory Open Access Journal |
| issn | 1674-9278 |
| language | English |
| last_indexed | 2024-03-13T08:31:50Z |
| publishDate | 2023-04-01 |
| publisher | KeAi Communications Co., Ltd. |
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| series | Advances in Climate Change Research |
| spelling | doaj.art-52061581c59e49ad8573de8715d89f712023-05-31T04:44:00ZengKeAi Communications Co., Ltd.Advances in Climate Change Research1674-92782023-04-01142286299Spatial and temporal variations of gross primary production simulated by land surface model BCC_AVIM2.0Wei-Ping Li0Yan-Wu Zhang1Mingquan Mu2Xue-Li Shi3Wen-Yan Zhou4Jin-Jun Ji5Earth System Modeling and Prediction Centre, China Meteorological Administration, Beijing 100081, China; State Key Laboratory of Severe Weather (LaSW), China Meteorological Administration, Beijing 100081, China; Corresponding author. Earth System Modeling and Prediction Centre, China Meteorological Administration, Beijing 100081, China.Earth System Modeling and Prediction Centre, China Meteorological Administration, Beijing 100081, China; State Key Laboratory of Severe Weather (LaSW), China Meteorological Administration, Beijing 100081, ChinaDepartment of Earth System Science, University of California, Irvine CA 92697-3100, USAEarth System Modeling and Prediction Centre, China Meteorological Administration, Beijing 100081, China; State Key Laboratory of Severe Weather (LaSW), China Meteorological Administration, Beijing 100081, ChinaEarth System Modeling and Prediction Centre, China Meteorological Administration, Beijing 100081, China; State Key Laboratory of Severe Weather (LaSW), China Meteorological Administration, Beijing 100081, ChinaInstitute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, ChinaGross primary production (GPP) is the largest flux and a crucial player in the terrestrial carbon cycle and has been studied extensively, yet large uncertainties remain in the spatiotemporal patterns of GPP in both observations and simulations. This study evaluates the performance of the second version of the Beijing Climate Center Atmosphere−Vegetation Interaction Model (BCC_AVIM2.0) in simulating GPP on multiple spatial and temporal scales in the Coupled Model Intercomparison Project Phase 6 (CMIP6) experiments. Model simulations driven by two meteorological datasets were compared with two observation-based GPP products covering 1982–2008. Spatial patterns of annual GPP show a significant latitudinal gradient in each dataset, increasing from cold (tundra) and dry (desert) biomes to warm (temperate) and humid (tropical rainforest) biomes. BCC_AVIM2.0 overestimates GPP in most parts of the globe, especially in boreal forest regions and Southeast China, while underestimating GPP in subhumid regions in eastern South America and tropical Africa. The four datasets broadly agree on the GPP seasonal cycle, but BCC_AVIM2.0 predicts an earlier beginning of spring growth and a larger amplitude of seasonal variations than those in the observations. The observation-based datasets exhibit slight interannual variability (IAV) and weak GPP linear trends, while the BCC_AVIM2.0 simulations demonstrate relatively large year-to-year variability and significant trends in the low-latitudes and temperate monsoon regions in North America and East Asia. Regarding the possible relationships between annual means of GPP and climate factors, BCC_AVIM2.0 predicts more extensive regions of the globe where the IAV of annual GPP is dominated by precipitation, especially in mid-to-high latitudes of the Northern Hemisphere and tropical Africa, while the observed GPP in the above regions is temperature- or radiation-dominant. The positive GPP biases due to earlier spring growth in boreal forest regions and negative GPP biases in off-equator tropical areas in the BCC_AVIM2.0 simulations imply that cold stress on biomes in boreal mid-to-high latitudes should be strengthened to restrain plant growth, while drought stress in low-latitude regions might be eased to enhance plant production in the future version of BCC_AVIM.http://www.sciencedirect.com/science/article/pii/S1674927823000333Gross primary productionSeasonal cycleInterannual variabilityTrendLand surface modelCMIP6 |
| spellingShingle | Wei-Ping Li Yan-Wu Zhang Mingquan Mu Xue-Li Shi Wen-Yan Zhou Jin-Jun Ji Spatial and temporal variations of gross primary production simulated by land surface model BCC_AVIM2.0 Advances in Climate Change Research Gross primary production Seasonal cycle Interannual variability Trend Land surface model CMIP6 |
| title | Spatial and temporal variations of gross primary production simulated by land surface model BCC_AVIM2.0 |
| title_full | Spatial and temporal variations of gross primary production simulated by land surface model BCC_AVIM2.0 |
| title_fullStr | Spatial and temporal variations of gross primary production simulated by land surface model BCC_AVIM2.0 |
| title_full_unstemmed | Spatial and temporal variations of gross primary production simulated by land surface model BCC_AVIM2.0 |
| title_short | Spatial and temporal variations of gross primary production simulated by land surface model BCC_AVIM2.0 |
| title_sort | spatial and temporal variations of gross primary production simulated by land surface model bcc avim2 0 |
| topic | Gross primary production Seasonal cycle Interannual variability Trend Land surface model CMIP6 |
| url | http://www.sciencedirect.com/science/article/pii/S1674927823000333 |
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