Search for: All records

Abstract. Methane (CH4) flux estimates from high-latitude North American wetlands remain highly uncertain in magnitude, seasonality, and spatial distribution. In this study, we evaluate a decade (2007–2017) of CH4 flux estimates by comparing 16 process-based models with atmospheric CH4 observations collected from in situ towers. We compare the Global Carbon Project (GCP) process-based models with a model inter-comparison from a decade earlier called The Wetland and Wetland CH4 Intercomparison of Models Project (WETCHIMP). Our analysis reveals that the GCP models have a much smaller inter-model uncertainty and have an average magnitude that is a factor of 1.5 smaller across Canada and Alaska. However, current GCP models likely overestimate wetland fluxes by a factor of two or more across Canada and Alaska based on tower-based atmospheric CH4 observations. The differences in flux magnitudes among GCP models are more likely driven by uncertainties in the amount of soil carbon or spatial extent of inundation than in temperature relationships, such as Q10 factors. The GCP models do not agree on the timing and amplitude of the seasonal cycle, and we find that models with a seasonal peak in July and August show the best agreement with atmospheric observations. Models that exhibit the best fit to atmospheric observation also have a similar spatial distribution; these models concentrate fluxes near Canada's Hudson Bay Lowlands. Current, state-of-the-art process-based models are much more consistent with atmospheric observations than models from a decade ago, but our analysis shows that there are still numerous opportunities for improvement. 

Abstract Under increasingly variable rainfall, trends toward more intense and less frequent daily‐scale precipitation have been identified using regional and global averages. However, it has not been explicitly demonstrated whether and where these trends are co‐located, which is important given their potential impacts on land surface processes. Here, using global observation and model‐based data sets, we find that trends toward fewer, larger daily precipitation events are common and relatively distributed across terrestrial ecosystems; they are approximately as common as trends toward more, larger daily precipitation events (which underpin increases in annual precipitation totals). Therefore, widespread precipitation intensification is not consistently increasing annual precipitation totals partly because precipitation events, especially of small‐to‐moderate depths (<10 mm/day), are simultaneously becoming less frequent. Independent of the consequences of changes in mean annual precipitation, these daily‐scale precipitation alterations can substantially impact water resource availability, floods, land‐atmosphere interactions, crop yields, wildfire fuel loads, and carbon sequestration. 

Abstract Dryland ecosystems cover 40% of our planet's land surface, support billions of people, and are responding rapidly to climate and land use change. These expansive systems also dominate core aspects of Earth's climate, storing and exchanging vast amounts of water, carbon, and energy with the atmosphere. Despite their indispensable ecosystem services and high vulnerability to change, drylands are one of the least understood ecosystem types, partly due to challenges studying their heterogeneous landscapes and misconceptions that drylands are unproductive “wastelands.” Consequently, inadequate understanding of dryland processes has resulted in poor model representation and forecasting capacity, hindering decision making for these at‐risk ecosystems. NASA satellite resources are increasingly available at the higher resolutions needed to enhance understanding of drylands' heterogeneous spatiotemporal dynamics. NASA's Terrestrial Ecology Program solicited proposals for scoping a multi‐year field campaign, of which Adaptation and Response in Drylands (ARID) was one of two scoping studies selected. A primary goal of the scoping study is to gather input from the scientific and data end‐user communities on dryland research gaps and data user needs. Here, we provide an overview of the ARID team's community engagement and how it has guided development of our framework. This includes an ARID kickoff meeting with over 300 participants held in October 2023 at the University of Arizona to gather input from data end‐users and scientists. We also summarize insights gained from hundreds of follow‐up activities, including from a tribal‐engagement focused workshop in New Mexico, conference town halls, intensive roundtables, and international engagements. 

Abstract Wetlands are the largest natural source of methane, yet bottom‐up models and top‐down models do not agree on global wetland methane emissions. In this study, we use TROPOMI methane data and inverse modeling to estimate the spatial and temporal distribution of global wetland methane emissions during the years 2019–2020 and compare inverse modeling results with an ensemble of 16 bottom‐up wetland models from the Global Carbon Project (GCP). We find that our inverse model increases wetland methane emissions near the equator (0–15) by 7% and decreases emissions in mid‐ and high‐latitude regions (31–90) by 26% compared to the mean of the GCP models. We also find that our inverse modeling estimate exhibits little seasonality in wetland methane emissions across most tropical wetland regions, even when emissions estimates within the prior include seasonality. This result is consistent with some bottom‐up models but not others. For mid‐ and high‐latitude wetland regions (e.g., the West Siberian lowland and Hudson Bay Great Lakes region), the seasonality of our inverse emissions estimate is consistent with most GCP models and suggests wetland methane emissions peak in July. Furthermore, we argue that an inundation map with accurate seasonality is a prerequisite for obtaining a bottom‐up methane emission estimate with appropriate seasonality. Overall, many of the bottom‐up models examined in this study agree with the magnitude and seasonality of the inverse model in major wetland regions, but there are nonetheless many opportunities to improve convergence between the bottom‐up and top‐down estimates. 

Abstract The northern permafrost region has been projected to shift from a net sink to a net source of carbon under global warming. However, estimates of the contemporary net greenhouse gas (GHG) balance and budgets of the permafrost region remain highly uncertain. Here, we construct the first comprehensive bottom‐up budgets of CO₂, CH₄, and N₂O across the terrestrial permafrost region using databases of more than 1000 in situ flux measurements and a land cover‐based ecosystem flux upscaling approach for the period 2000–2020. Estimates indicate that the permafrost region emitted a mean annual flux of 12 (−606, 661) Tg CO₂–C yr⁻¹, 38 (22, 53) Tg CH₄–C yr⁻¹, and 0.67 (0.07, 1.3) Tg N₂O–N yr⁻¹to the atmosphere throughout the period. Thus, the region was a net source of CH₄and N₂O, while the CO₂balance was near neutral within its large uncertainties. Undisturbed terrestrial ecosystems had a CO₂sink of −340 (−836, 156) Tg CO₂–C yr⁻¹. Vertical emissions from fire disturbances and inland waters largely offset the sink in vegetated ecosystems. When including lateral fluxes for a complete GHG budget, the permafrost region was a net source of C and N, releasing 144 (−506, 826) Tg C yr⁻¹and 3 (2, 5) Tg N yr⁻¹. Large uncertainty ranges in these estimates point to a need for further expansion of monitoring networks, continued data synthesis efforts, and better integration of field observations, remote sensing data, and ecosystem models to constrain the contemporary net GHG budgets of the permafrost region and track their future trajectory. 

Abstract. Wetlands are the largest natural source of methane (CH4) emissions globally. Northern wetlands (>45° N), accounting for 42 % of global wetland area, are increasingly vulnerable to carbon loss, especially as CH4 emissions may accelerate under intensified high-latitude warming. However, the magnitude and spatial patterns of high-latitude CH4 emissions remain relatively uncertain. Here, we present estimates of daily CH4 fluxes obtained using a new machine learning-based wetland CH4 upscaling framework (WetCH4) that combines the most complete database of eddy-covariance (EC) observations available to date with satellite remote-sensing-informed observations of environmental conditions at 10 km resolution. The most important predictor variables included near-surface soil temperatures (top 40 cm), vegetation spectral reflectance, and soil moisture. Our results, modeled from 138 site years across 26 sites, had relatively strong predictive skill, with a mean R2 of 0.51 and 0.70 and a mean absolute error (MAE) of 30 and 27 nmol m−2 s−1 for daily and monthly fluxes, respectively. Based on the model results, we estimated an annual average of 22.8±2.4 Tg CH4 yr−1 for the northern wetland region (2016–2022), and total budgets ranged from 15.7 to 51.6 Tg CH4 yr−1, depending on wetland map extents. Although 88 % of the estimated CH4 budget occurred during the May–October period, a considerable amount (2.6±0.3 Tg CH4) occurred during winter. Regionally, the Western Siberian wetlands accounted for a majority (51 %) of the interannual variation in domain CH4 emissions. Overall, our results provide valuable new high-spatiotemporal-resolution information on the wetland emissions in the high-latitude carbon cycle. However, many key uncertainties remain, including those driven by wetland extent maps and soil moisture products and the incomplete spatial and temporal representativeness in the existing CH4 flux database; e.g., only 23 % of the sites operate outside of summer months, and flux towers do not exist or are greatly limited in many wetland regions. These uncertainties will need to be addressed by the science community to remove the bottlenecks currently limiting progress in CH4 detection and monitoring. The dataset can be found at https://doi.org/10.5281/zenodo.10802153 (Ying et al., 2024).