Detecting robust signals of interannual variability of gross primary productivity in Asia from multiple terrestrial carbon cycle models and long-term satellite-based vegetation data

Friday, 19 December 2014: 2:40 PM
Kazuhito Ichii1,2, Masayuki Kondo1, Masahito Ueyama3, Tomomichi Kato4, Akihiko Ito2, Takahiro Sasai5, Hisashi Sato1, Hideki Koayashi6 and Nobuko Saigusa2, (1)JAMSTEC Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan, (2)CGER-NIES, Tsukuba, Japan, (3)Osaka Prefecture University, Sakai, Japan, (4)Hokkaido University, Sapporo, Japan, (5)University of Tsukuba, Tsukuba, Japan, (6)Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
Long term record of satellite-based terrestrial vegetation are important to evaluate terrestrial carbon cycle models. In this study, we demonstrate how multiple satellite observation can be used for evaluating past changes in gross primary productivity (GPP) and detecting robust anomalies in terrestrial carbon cycle in Asia through our model-data synthesis analysis, Asia-MIP. We focused on the two different temporal coverages: long-term (30 years; 1982-2011) and decadal (10 years; 2001-2011; data intensive period) scales. We used a NOAA/AVHRR NDVI record for long-term analysis and multiple satellite data and products (e.g. Terra-MODIS, SPOT-VEGETATION) as historical satellite data, and multiple terrestrial carbon cycle models (e.g. BEAMS, Biome-BGC, ORCHIDEE, SEIB-DGVM, and VISIT).

As a results of long-term (30 years) trend analysis, satellite-based time-series data showed that approximately 40% of the area has experienced a significant increase in the NDVI, while only a few areas have experienced a significant decreasing trend over the last 30 years. The increases in the NDVI were dominant in the sub-continental regions of Siberia, East Asia, and India. Simulations using the terrestrial biosphere models also showed significant increases in GPP, similar to the results for the NDVI, in boreal and temperate regions. A modeled sensitivity analysis showed that the increases in GPP are explained by increased temperature and precipitation in Siberia. Precipitation, solar radiation, CO2fertilization and land cover changes are important factors in the tropical regions. However, the relative contributions of each factor to GPP changes are different among the models.

Year-to-year variations of terrestrial GPP were overall consistently captured by the satellite data and terrestrial carbon cycle models if the anomalies are large (e.g. 2003 summer GPP anomalies in East Asia and 2002 spring GPP anomalies in mid to high latitudes). The behind mechanisms can be consistently explained by the models if the anomalies are caused in the low temperature regions (e.g. spring in Northern Asia). However, water-driven or radiation-driven GPP anomalies lacks consistent explanation among models. Therefore, terrestrial carbon cycle models require improvement of the sensitivity of climate anomalies to carbon cycles.