Higher temperature variability reduces temperature sensitivity of vegetation growth in Northern Hemisphere - Wu, Xiuchen; Liu, Hongyan; Li, Xiaoyan; Piao, Shilong; Ciais, Philippe; Guo, Weichao; Yin, Yi; Poulter, Ben; Peng, Changhui; Viovy, Nicolas; Vuichard, Nicolas; Wang, Pei; et al. (Geophysical Research Letters, 2017)
Abstract Interannual air temperature variability has changed over some regions in Northern Hemisphere (NH), accompanying with climate warming. However, whether and to what extent it regulates the interannual sensitivity of vegetation growth to temperature variability (i.e., interannual temperature sensitivity)—one central issue in understanding and predicting the responses of vegetation growth to changing climate—still remains poorly quantified and understood. Here we quantify the relationships between the interannual temperature sensitivity of mean growing‐season (April–October) normalized difference vegetation index (NDVI) and ecosystem model simulations of gross primary productivity (GPP), and variability in mean growing‐season temperature for forest, shrub, and grass over NH. We find that higher interannual variability in mean growing‐season temperature leads to consistent decrease in interannual temperature sensitivity of mean growing‐season NDVI among all vegetation types but not in model simulations of GPP. Drier condition associates with ~130 ± 150% further decrease in interannual temperature sensitivity of mean growing‐season NDVI by temperature variability in forest and shrub. These results illustrate that varying temperature variability can significantly regulate the interannual temperature sensitivity of vegetation growth over NH, interacted with drought variability and nonlinear responses of photosynthesis to temperature. Our findings call for an improved characterization of the nonlinear effects of temperature variability on vegetation growth within global ecosystem models. , Key Points It shows consistent decrease in temperature sensitivity of vegetation growth by temperature variability for all vegetation types Larger decrease in temperature sensitivity of vegetation growth by temperature variability is found in forest and shrub in dry regions Drier condition adds further decrease in temperature sensitivity of vegetation growth by temperature variability for forest and shrub in dry regions

Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates - Chang, Kuang‐Yu; Riley, William J.; Collier, Nathan; McNicol, Gavin; Fluet‐Chouinard, Etienne; Knox, Sara H.; Delwiche, Kyle B.; Jackson, Robert B.; Poulter, Benjamin; Saunois, Marielle; Chandra, Naveen; Gedney, Nicola; et al. (Global Change Biology, 2023)
Abstract The recent rise in atmospheric methane (CH 4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH 4 source, estimates of global wetland CH 4 emissions vary widely among approaches taken by bottom‐up (BU) process‐based biogeochemical models and top‐down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi‐model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH 4 emission estimates and model performance. We find that using better‐performing models identified by observational constraints reduces the spread of wetland CH 4 emission estimates by 62% and 39% for BU‐ and TD‐based approaches, respectively. However, global BU and TD CH 4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH 4 year −1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter‐site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH 4 models to move beyond static benchmarking and focus on evaluating site‐specific and ecosystem‐specific variabilities inferred from observations.