Process-based land surface models are important tools for estimating global wetland methane (CH4) emissions and projecting their behavior across space and time. So far there are no performance assessments of model responses to drivers at multiple time scales. In this study, we apply wavelet analysis to identify the dominant time scales contributing to model uncertainty in the frequency domain. We evaluate seven wetland models at 23 eddy covariance tower sites. Our study first characterizes site-level patterns of freshwater wetland CH4 fluxes (FCH4) at different time scales. A Monte Carlo approach has been developed to incorporate flux observation error to avoid misidentification of the time scales that dominate model error. Our results suggest that 1) significant model-observation disagreements are mainly at short- to intermediate time scales (< 15 days); 2) most of the models can capture the CH4 variability at long time scales (> 32 days) for the boreal and Arctic tundra wetland sites but have limited performance for temperate and tropical/subtropical sites; 3) model error approximates pink noise patterns, indicating that biases at short time scales (< 5 days) could contribute to persistent systematic biases on longer time scales; and 4) differences in error pattern are related to model structure (e.g. proxy of CH4 production). Our evaluation suggests the need to accurately replicate FCH4 variability in future wetland CH4 model developments.

Solar energy, if carefully planned, can contribute to the attainment of global climate mitigation goals by reducing reliance on fossil fuel energy. It has been suggested that large-scale photovoltaic solar farms envisioned over the Sahara desert would reduce surface albedo, leading to increased rainfall and vegetation cover that would benefit the regional environment while meeting the world’s energy demand. However, adverse remote effects resulting from atmospheric teleconnections could offset such regional benefits. We use state-of-the-art Earth-system model simulations to evaluate the global impacts of Sahara solar farms. Our results indicate a redistribution of precipitation causing Amazon droughts and forest degradation, and global surface temperature rise and sea-ice loss, particularly over the Arctic due to increased polarward heat transport, and northward expansion of deciduous forests in the Northern Hemisphere. We also identify reduced El Niño-Southern Oscillation and Atlantic Nino variability and enhanced tropical cyclone activity. All these remote effects are in line with the global impacts of the Sahara land-cover transition ~6,000 years ago when Sahara desert was wetter and greener. The improved understanding of the forcing mechanisms of massive Sahara solar farms can be helpful for the future site selection of large-scale desert solar energy facilities.