< p > 2011年奥尔顿等所使用的方法是适合本文的范围和目的,展示一个弱点的方法——但不是击球的综合分析模型。< / p > < p >我认为很有用的问题使用击球但不正确的结论,奥尔顿等要求击球不是有用的。奥尔顿等表明,反照率,净短波辐射,和径流PFT-specific四个参数的值不敏感,但他们也发现净碳平衡< em > < / em >敏感。< / p > < p >研究的结论仅限于朱尔斯模型评估和允许不同的参数(因此被反演估计)。有很多不同型号的陆地生态系统功能,和许多模型参数化方法。此外,朱尔斯有50多个参数为每个击球时,只有四个不同的在这个研究。(这是朱尔斯的参数的数量设置< a href = " http://www.geosci -模型- dev.net/7/361/2014/gmd - 7 - 361 - 2014 - supplement.pdf”rel = " nofollow noreferrer " > fortran名称列表下的< / > <代码> JULES_NVEGPARM < /代码>和<代码> JULES_PFTPARM < /代码>)。< / p > < p >本研究还提供了如何使用什么反转的例子基本上是“免费”参数(类似于‘平先验贝叶斯上下文)有一定的局限性。图3显示了这个,因为“检索”(inverse-estimated)参数与直接测量这些参数不一致。这更多的是一种估计PFT-level参数的反演方法的限制比击球的效用作为一个近似的植物生理多样性。< / p > < p >回答你的问题,是的,有替代模型结构和参数化方法。这是一个活跃的研究领域(和一个我感兴趣)。 Here are some examples, in order of increasing complexity (and data and computational requirements). - Global land surface models like JULES are necessarily more abstract (and use PFTs to represent a 'Biome').
- Represent groups of individuals that belong to a particular PFT and age class (This was first done in the Ecosystem Demography model (Moorecroft et al 2001, which has both evolved to ED2 and has been integrated into NCAR's Community Land Model.
- Vary parameters dynamically in both space and time. For example, Wang et al 2012 show that varying Vcmax in (vertical) space and time improve estimates of gross primary productivity in a very simple crop model.
- Individual Based Models (IBM's) represent individuals explicitly. These are reviewed by DeAngelis and Mooij 2005.
- Using probability distributions to reflect parameter variability in the system being studied. I am not aware that this has been attempted.
However, adding more degrees of freedom limits inference - too many unconstrained parameters make it difficult to parameterize the model, understand its output, or make general conclusions about how the world operates.
Remember "All models are wrong, but some are useful" - George Box