Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden.
Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark.
PLoS One. 2018 Jun 21;13(6):e0199383. doi: 10.1371/journal.pone.0199383. eCollection 2018.
Biogeochemical models use meteorological forcing data derived with different approaches (e.g. based on interpolation or reanalysis of observation data or a hybrid hereof) to simulate ecosystem processes such as gross primary productivity (GPP). This study assesses the impact of different widely used climate datasets on simulated gross primary productivity and evaluates the suitability of them for reproducing the global and regional carbon cycle as mapped from independent GPP data. We simulate GPP with the biogeochemical model LPJ-GUESS using six historical climate datasets (CRU, CRUNCEP, ECMWF, NCEP, PRINCETON, and WFDEI). The simulated GPP is evaluated using an observation-based GPP product derived from eddy covariance measurements in combination with remotely sensed data. Our results show that all datasets tested produce relatively similar GPP simulations at a global scale, corresponding fairly well to the observation-based data with a difference between simulations and observations ranging from -50 to 60 g m-2 yr-1. However, all simulations also show a strong underestimation of GPP (ranging from -533 to -870 g m-2 yr-1) and low temporal agreement (r < 0.4) with observations over tropical areas. As the shortwave radiation for tropical areas was found to have the highest uncertainty in the analyzed historical climate datasets, we test whether simulation results could be improved by a correction of the tested shortwave radiation for tropical areas using a new radiation product from the International Satellite Cloud Climatology Project (ISCCP). A large improvement (up to 48%) in simulated GPP magnitude was observed with bias corrected shortwave radiation, as well as an increase in spatio-temporal agreement between the simulated GPP and observation-based GPP. This study conducts a spatial inter-comparison and quantification of the performances of climate datasets and can thereby facilitate the selection of climate forcing data over any given study area for modelling purposes.
生物地球化学模型使用不同方法（例如，基于观测数据的插值或再分析，或混合方法）得出的气象强迫数据来模拟生态系统过程，如总初级生产力（GPP）。本研究评估了不同广泛使用的气候数据集对模拟总初级生产力的影响，并评估了它们在再现独立 GPP 数据映射的全球和区域碳循环方面的适用性。我们使用生物地球化学模型 LPJ-GUESS 模拟 GPP，使用六个历史气候数据集（CRU、CRUNCEP、ECMWF、NCEP、PRINCETON 和 WFDEI）。使用基于涡度协方差测量的与遥感数据相结合的观测 GPP 产品评估模拟 GPP。我们的结果表明，所有测试数据集在全球范围内产生相对相似的 GPP 模拟，与基于观测的数据相当吻合，模拟与观测之间的差异范围为-50 到 60 g m-2 yr-1。然而，所有模拟也显示出对 GPP 的强烈低估（范围从-533 到-870 g m-2 yr-1）和与热带地区观测数据的低时间一致性（r < 0.4）。由于分析的历史气候数据集中热带地区的短波辐射具有最高的不确定性，我们测试了通过使用国际卫星云气候学计划（ISCCP）的新辐射产品校正热带地区的测试短波辐射，是否可以改善模拟结果。校正热带地区的短波辐射后，模拟 GPP 幅度有了很大提高（高达 48%），并且模拟 GPP 与基于观测的 GPP 之间的时空一致性也有所提高。本研究进行了气候数据集的空间比较和性能量化，从而可以促进为建模目的选择任何给定研究区域的气候强迫数据。
