Wetland emissions contribute the largest uncertainties to the current global atmospheric CH₄ budget, and how these emissions will change under future climate scenarios is also still poorly understood. Bloom et al. (2017b) developed WetCHARTs, a simple, data-driven, ensemble-based model that produces estimates of CH₄ wetland emissions constrained by observations of precipitation and temperature. This study performs the first detailed global and regional evaluation of the WetCHARTs CH₄ emission model ensemble against 9 years of high-quality, validated atmospheric CH₄ observations from GOSAT (the Greenhouse Gases Observing Satellite). A 3-D chemical transport model is used to estimate atmospheric CH₄ mixing ratios based on the WetCHARTs emissions and other sources. Across all years and all ensemble members, the observed global seasonal-cycle amplitude is typically underestimated by WetCHARTs by −7.4 ppb, but the correlation coefficient of 0.83 shows that the seasonality is well-produced at a global scale. The Southern Hemisphere has less of a bias (−1.9 ppb) than the Northern Hemisphere (−9.3 ppb), and our findings show that it is typically the North Tropics where this bias is the worst (−11.9 ppb). We find that WetCHARTs generally performs well in reproducing the observed wetland CH₄ seasonal cycle for the majority of wetland regions although, for some regions, regardless of the ensemble configuration, WetCHARTs does not reproduce the observed seasonal cycle well. In order to investigate this, we performed detailed analysis of some of the more challenging exemplar regions (Paraná River, Congo, Sudd and Yucatán). Our results show that certain ensemble members are more suited to specific regions, due to either deficiencies in the underlying data driving the model or complexities in representing the processes involved. In particular, incorrect definition of the wetland extent is found to be the most common reason for the discrepancy between the modelled and observed CH₄ concentrations. The remaining driving data (i.e. heterotrophic respiration and temperature) are shown to also contribute to the mismatch with observations, with the details differing on a region-by-region basis but generally showing that some degree of temperature dependency is better than none. We conclude that the data-driven approach used by WetCHARTs is well-suited to producing a benchmark ensemble dataset against which to evaluate more complex process-based land surface models that explicitly model the hydrological behaviour of these complex wetland regions.

中文翻译：

---

使用GOSAT观测资料探索对湿地甲烷排放系综（WetCHART）的约束

湿地排放给当前全球大气CH ₄预算带来最大的不确定性，人们对这些排放在未来气候情景下的变化方式仍知之甚少。Bloom等。（2017b）开发了WetCHARTs，这是一个简单的，数据驱动的基于集合的模型，该模型产生了受降水和温度观测值约束的CH ₄湿地排放估算。这项研究针对9年的高质量，经过验证的大气CH ₄进行了WetCHARTs CH ₄排放模型集合的首次详细的全球和区域评估GOSAT（温室气体观测卫星）的观测结果。使用3-D化学迁移模型基于WetCHART的排放量和其他来源估算大气中的CH ₄混合比。在所有年份和所有集合成员，所观察到的全球季节周期振幅典型地通过由WetCHARTs低估- 7.4 ppb的，但示出了0.83的相关系数，所述季节性是在全球范围内精心制作。南半球有偏差（在小于-1.9 比北半球PPB）（- 9.3 ppb的），和我们的研究结果表明它是典型的北热带地区这种偏见是最差的（-11.9 ppb）。我们发现，对于大多数湿地地区，WetCHARTs在再现观察到的湿地CH ₄季节周期方面通常表现良好，尽管在某些地区，无论整体结构如何，WetCHARTs都无法很好地再现观察到的季节周期。为了对此进行调查，我们对一些更具挑战性的典型地区（巴拉那河，刚果，苏德和尤卡坦州）进行了详细分析。我们的结果表明，由于驱动模型的基础数据不足或表示涉及的过程很复杂，因此某些集合成员更适合特定区域。特别是，发现湿地范围的定义不正确是造成模拟CH与观测CH之间差异的最常见原因。_4个浓度。剩余的行驶数据（即异养呼吸和温度）也显示出与观测值不匹配的原因，每个区域的细节有所不同，但通常显示出一定程度的温度依赖性比没有更好。我们得出的结论是，WetCHART使用的数据驱动方法非常适合生成基准集合数据集，以此为基础评估更复杂的基于过程的地表模型，这些模型可以显式地模拟这些复杂湿地区域的水文行为。