Representations of the seasonal peak uptake of CO₂ and climate extremes effects have important implications for accurately estimating annual magnitude and inter-annual variations of terrestrial carbon fluxes, however the consistency of such representations among different satellite models and process-based (PB) models remain poorly known. Here we investigated these issues over North America based on a large ensemble of state-of-the-art gross primary production (GPP) models, including two solar-induced chlorophyll fluorescence (SIF)-based models (WECANN and GOPT), three remote sensing driven light-use efficiency (LUE) models, and 10 PB models. We found that the two SIF-based GPP estimates were bilaterally consistent in spatial patterns of peak growing season GPP (GPP_PGS; with the largest uptake at the Corn-Belt area in the United States) and climate extremes-driven responses. The simulations from three LUE models showed relatively consistent spatial patterns of GPP_PGS and climate extremes-driven responses, which agreed well with SIF-based estimates and satellite based metrics. Obviously differed from SIF and LUE based estimates, the simulations from PB models exhibited noticeable divergences and mostly failed to reasonably replicate the spatial pattern of GPP_PGS. In addition, satellite models and PB models were comparably able to capture the effects of climate extremes on GPP, but showing obvious divergences in the magnitude of impacts among different models, and the former outperformed the latter in locating GPP changes caused by climate extremes. We discussed the possible origins of such discrepancies in state-of-the-art models with focus on PB models. Improving the parameterizations of critical variables (e.g. leaf area index) and better characterizing environmental stresses could lead to more robust estimates of large-scale terrestrial GPP with PB models, thus serving for accurately assessing global carbon budget and better understanding the impacts of climate change on the terrestrial carbon cycle. Our study offers a baseline for improving large-scale estimation of terrestrial GPP.

CO ₂的季节性峰值吸收和气候极端效应的表示形式对准确估算陆地碳通量的年际变化和年际变化具有重要意义，但是不同卫星模型和基于过程的模型（PB）之间这种表示形式的一致性仍然存在鲜为人知。在这里，我们基于大型的最新总初级生产（GPP）模型对北美地区的这些问题进行了调查，其中包括两个基于太阳诱导叶绿素荧光（SIF）的模型（WECANN和GOPT），三个远程感应驱动的光使用效率（LUE）模型和10 PB模型。我们发现，两个基于SIF的GPP估算在高峰生长季节GPP（GPP _PGS; （在美国的玉米带地区吸收最多）和气候极端驱动的响应。来自三个LUE模型的仿真显示了GPP _PGS的相对一致的空间模式和气候极端驱动的响应，这与基于SIF的估计和基于卫星的度量非常吻合。显然与基于SIF和LUE的估计有所不同，PB模型的仿真表现出明显的差异，并且大多无法合理地复制GPP _PGS的空间模式。此外，卫星模型和PB模型能够比较地捕捉到极端气候对GPP的影响，但在不同模型之间的影响程度方面显示出明显的差异，在定位极端气候导致的GPP变化方面，前者的性能优于后者。我们讨论了最新模型中此类差异的可能来源，重点是PB模型。改善关键变量（例如叶面积指数）的参数化并更好地表征环境压力，可能会导致使用PB模型对大型陆地GPP进行更可靠的估算，从而有助于准确评估全球碳预算并更好地了解气候变化对气候变化的影响陆地碳循环。我们的研究为改善大规模地面GPP估算提供了基线。