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Nitrous oxide (N₂O) has a global warming potential that is 300 times that of carbon dioxide on a 100-y timescale, and is of major importance for stratospheric ozone depletion. The climate sensitivity of N₂O emissions is poorly known, which makes it difficult to project how changing fertilizer use and climate will impact radiative forcing and the ozone layer. Analysis of 6 y of hourly N₂O mixing ratios from a very tall tower within the US Corn Belt—one of the most intensive agricultural regions of the world—combined with inverse modeling, shows large interannual variability in N₂O emissions (316 Gg N₂O-N·y−1 to 585 Gg N₂O-N·y−1). This implies that the regional emission factor is highly sensitive to climate. In the warmest year and spring (2012) of the observational period, the emission factor was 7.5%, nearly double that of previous reports. Indirect emissions associated with runoff and leaching dominated the interannual variability of total emissions. Under current trends in climate and anthropogenic N use, we project a strong positive feedback to warmer and wetter conditions and unabated growth of regional N₂O emissions that will exceed 600 Gg N₂O-N·y−1, on average, by 2050. This increasing emission trend in the US Corn Belt may represent a harbinger of intensifying N₂O emissions from other agricultural regions. Such feedbacks will pose a major challenge to the Paris Agreement, which requires large N₂O emission mitigation efforts to achieve its goals.

The effects of dams on carbon dioxide (CO2) fluxes in downstream lakes remain elusive. Here we combined eddy covariance observations and random forest models to examine multi‐decadal variations in CO2 fluxes in the Poyang Lake, the largest freshwater lake in China, and quantified the contribution of the Three Gorges Dam (TGD), the world's largest hydraulic project. We found the lake fluctuated between CO2 source and sink in 1961–2016, and tended to be CO2 sink in the post‐TGD period (2003–2016) when vegetation expanded early and spatially due to declining water level. TGD can explain approximately 6% of the total differences in annual CO2 fluxes, with major contributions in the impoundment period (up to 22% in middle September to October). The results show a positive side of operational major hydraulic projects on lake carbon sink, and probably caution the negative side of carbon release after dam removal. Plain Language Summary In the past century, dams have significantly altered the hydrological connectivity between rivers and lakes, which affect CO2 exchange in the downstream lake systems. As the largest freshwater lake in China, Poyang Lake has also undergone drastic hydrological changes, attributable largely to the operation of the Three Gorges Dam (TGD), the world's largest hydraulic project ever, in 2003. Based on flux observations and machine learning method, we show that annual lake CO2 exchange shifted toward carbon sink during 1961–2016. The TGD has a major impact on lake CO2 fluxes, especially during the impoundment stage in middle September–October, explaining 22% of the flux differences between the pre‐ and post‐TGD period. The results show a positive side of hydraulic projects albeit their adverse impact on ecological protection. Key Points Poyang Lake as a CO2 source or sink significantly depends on water level Poyang Lake became a CO2 sink since the Three Gorges Dam operation in 2003 Dam explains 22% of differences in CO2 fluxes in autumn impoundment period

Ammonia (NH.sub.3 ), a key alkaline gas in the atmosphere, significantly influences ecosystem nitrogen cycling and the formation of fine particulate matter (PM.sub.2.5). However, limited ground-based monitoring hinders understanding of NH.sub.3 's spatial and temporal dynamics and its dry deposition across China, which is ranked as one of the largest global NH.sub.3 emission hotspots. This study integrated 2013-2023 satellite-derived NH.sub.3 column concentrations from the Cross-track Infrared Sounder (CrIS) with adjustments from approximately five years ground in-situ ground observations to derive spatial-temporal variation in ground-level NH.sub.3 concentrations across China. We also used the GEOS-Chem transport model and a random forest algorithm by using emission inventories and reanalysis meteorological fields to simulate NH.sub.3 dry deposition velocity and fluxes, and explore the mechanisms driving observed trends. The CrIS observations results show that column-averaged (averages from ground to â¼ 1 km) NH.sub.3 concentrations were the highest in the North China Plain ( 10 ppb), with notable annual and seasonal increasing trends. NH.sub.3 concentrations in 2023 were 13.8 %-30.6 % higher than in 2013. CrIS retrievals aligned well with in-situ data, though were generally about twice as high. After applying the regression equation between ground in-situ observations and CrIS column-averaged NH.sub.3 concentrations, we derive the spatial-temporal ground-level (1-1.5 m) NH.sub.3 concentrations and dry deposition fluxes from 2013 to 2023. The NH.sub.3 dry deposition fluxes exhibited a clear east-west gradient, with maxima in the North China Plain, and another hotpot region is also observed in the Sichuan Basin, southwestern China. Increases in ground-level NH.sub.3 concentrations and deposition were most pronounced in urban, cropland, and forest regions, with urban areas experiencing the fastest growth and grasslands the highest total deposition. The national mean ground-level NH.sub.3 concentration and dry deposition flux were 4.98 ppb and 0.51 g NH.sub.3 m.sup.-2 yr.sup.-1, respectively. Anthropogenic emissions explained 77.4 % of the variability in ground-level NH.sub.3 concentration trend, and meteorological factors accounted for the remainder. Besides, 72.6 %-81.2 % of the NH.sub.3 dry deposition trend was governed by NH.sub.3 concentration changes. This study identifies the underlying cause of increasing ammonia pollution, which can be used to better inform nitrogen management strategies in China.

Agricultural nitrous oxide (N2O) emission accounts for a non-trivial fraction of global greenhouse gas (GHG) budget. To date, estimatingN2O fluxes from cropland remains a challenging task because the related microbial processes (e.g., nitrification and denitrification) are controlled by complex interactions among climate, soil, plant and human activities. Existing approaches such as process-based (PB) models have well-known limitations due to insufficient representations of the processes or uncertainties of model parameters, and due to leverage recent advances in machine learning (ML) a new method is needed to unlock the “black box” to overcome its limitations such as low interpretability, out-of-sample failure and massive data demand. In this study, we developed a first-of-its-kind knowledge-guided machine learning model for agroecosystems (KGML-ag) by incorporating biogeophysical and chemical domain knowledge from an advanced PB model, ecosys, and tested it by comparing simulating daily N2O fluxes with real observed data from mesocosm experiments. The gated recurrent unit (GRU) was used as the basis to build the model structure. To optimize the model performance, we have investigated a range of ideas, including (1) using initial values of intermediate variables (IMVs) instead of time series as model input to reduce data demand; (2) building hierarchical structures to explicitly estimate IMVs for further N2O prediction; (3) using multi-task learning to balance the simultaneous training on multiple variables; and (4) pre-training with millions of synthetic data generated from ecosys and fine-tuning with mesocosm observations. Six other pure ML models were developed using the same mesocosm data to serve as the benchmark for the KGML-ag model. Results show that KGML-ag did an excellent job in reproducing the mesocosmN2O fluxes (overall r2=0.81, and RMSE=3.6 mgNm-2d-1 from cross validation). Importantly, KGML-ag always outperforms the PB model and ML models in predicting N2O fluxes, especially for complex temporal dynamics and emission peaks. Besides, KGML-ag goes beyond the pure ML models by providing more interpretable predictions as well as pinpointing desired new knowledge and data to further empower the current KGML-ag. We believe the KGML-ag development in this study will stimulate a new body of research on interpretable ML for biogeochemistry and other related geoscience processes.

Water returned to the atmosphere as evapotranspiration (ET) is approximately 1.6x global river discharge and has wide‐reaching impacts on groundwater and streamflow. In the U.S. Midwest, widespread land conversion from prairie to pasture to cropland has altered spatiotemporal patterns of ET, yet there is not consensus on the direction of change or the mechanisms controlling changes. We measured ET at three locations within the Long‐Term Agroecosystem Research network along a latitudinal gradient with paired rainfed cropland and prairie sites at each location. At the northern locations, the Upper Mississippi River Basin (UMRB) and Kellogg Biological Station (KBS), the cropland has annual ET that is 84 and 29 mm/year (22% and 5%) higher, respectively, caused primarily by higher ET during springtime when fields are fallow. At the southern location, the Central Mississippi River Basin (CMRB), the prairie has 69 mm/year (11%) higher ET, primarily due to a longer growing season. Differences in climate and that the CMRB prairie is remnant native prairie, while the UMRB and KBS prairies are restored, make it challenging to attribute differences to specific mechanisms. To accomplish this, we examine the energy balance using the Two‐Resistance Method (TRM). Results from the TRM demonstrate that higher surface conductance in croplands is the primary factor leading to higher springtime ET from croplands, relative to prairies. Results from this study provide insight into impacts of warm season grasses on the hydrology of the U.S. Corn Belt by providing a mechanistic understanding of how land use change affects the water budget. Plain Language Summary Evapotranspiration (ET) consists of evaporation from bare soil and plant leaves. ET is ∼1.6x greater than global river flow and has wide‐reaching impacts on groundwater and streamflow. In the U.S. Midwest, widespread land conversion from prairies to croplands has altered patterns of ET, yet there is no consensus on the direction of this change or the mechanisms controlling changes. In this study we use measurements of ET at three locations within the Long‐Term Agroecosystem Research (LTAR) network that have paired cropland and prairie sites. Surprisingly, we found that in the two northern sites, the croplands had higher ET than the prairies, particularly during springtime when the croplands are fallow. We used mathematical analysis of the energy budget to show that a parameter called the surface conductance controls the differences in ET between the croplands and prairies. During springtime in prairies, the standing, dormant vegetation blocks transfer of water vapor from the land surface, reducing the surface conductance, and limits the ET. Results from this study provide insight into the impact of land conversion from prairies to croplands on the hydrology of the U.S. Corn Belt by providing a mechanistic understanding of how land use change affects the water budget. Key Points Differences in evapotranspiration between croplands and prairies was quantified by a mechanistic Two Resistance Method Bowen ratio during springtime is higher in prairies than croplands Surface resistance is the primary factor causing springtime evapotranspiration differences between croplands and prairies

We present top-down constraints on global monthly N2O emissions for 2011 from a multi-inversion approach and an ensemble of surface observations. The inversions employ the GEOS-Chem adjoint and an array of aggregation strategies to test how well current observations can constrain the spatial distribution of global N2O emissions. The strategies include (1) a standard 4D-Var inversion at native model resolution (4∘×5∘), (2) an inversion for six continental and three ocean regions, and (3) a fast 4D-Var inversion based on a novel dimension reduction technique employing randomized singular value decomposition (SVD). The optimized global flux ranges from 15.9 TgNyr-1 (SVD-based inversion) to 17.5–17.7 TgNyr-1 (continental-scale, standard 4D-Var inversions), with the former better capturing the extratropicalN2O background measured during the HIAPER Pole-to-Pole Observations (HIPPO) airborne campaigns. We find that the tropics provide a greater contribution to the global N2O flux than is predicted by the prior bottom-up inventories, likely due to underestimated agricultural and oceanic emissions. We infer an overestimate of natural soil emissions in the extratropics and find that predicted emissions are seasonally biased in northern midlatitudes. Here, optimized fluxes exhibit a springtime peak consistent with the timing of spring fertilizer and manure application, soil thawing, and elevated soil moisture. Finally, the inversions reveal a major emission underestimate in the US Corn Belt in the bottom-up inventory used here. We extensively test the impact of initial conditions on the analysis and recommend formally optimizing the initial N2O distribution to avoid biasing the inferred fluxes. We find that the SVD-based approach provides a powerful framework for deriving emission information from N2O observations: by defining the optimal resolution of the solution based on the information content of the inversion, it provides spatial information that is lost when aggregating to political or geographic regions, while also providing more temporal information than a standard 4D-Var inversion.

Deuterium‐excess (d) in water is a combination of the oxygen (δ18O) and hydrogen (δD) isotope ratios, and its variability is thought to indicate the location and environmental conditions of the marine moisture source. In this study, we analyze d of water vapor (dv) from six sites, all between 37 and 44°N to examine patterns in the atmospheric surface layer and identify the main drivers of variability. Two sites are in urban settings (New Haven, CT, USA and Beijing, China), two sites are in agricultural settings (Rosemount, MN, USA and Luancheng, China), and two sites are in natural ecosystems, a forest (Borden Forest, Ontario, Canada) and a grassland (Duolun, China). We found a robust diurnal cycle in dvat all sites with maximum values during mid‐day. Isotopic land surface model simulations suggest that plant transpiration is one mechanism underlying the diurnal pattern. An isotopic large‐eddy simulation model shows that entrainment of the free atmosphere into the boundary layer can also produce highdvvalues in mid‐day. Daily mid‐day means ofdvwere negatively correlated with local mid‐day relative humidity and positively correlated with planetary boundary layer height at the North American sites, but not the Chinese sites. The mechanism for these differences is still undetermined. These results demonstrate that within the diurnal time scale,dv of the surface air at continental locations can be significantly altered by local processes, and is therefore not a conserved tracer of humidity from the marine moisture source region as has previously been assumed. Key Points There is a diurnal cycle in d‐excess with maxima during mid‐day at all sites D‐excess is correlated with humidity and boundary layer height at some sites Models show transpiration and entrainment contribute to the diurnal cycle

Increasing atmospheric humidity and convective precipitation over land provide evidence of intensification of the hydrologic cycle – an expected response to surface warming. The extent to which terrestrial ecosystems modulate these hydrologic factors is important to understand feedbacks in the climate system. We measured the oxygen and hydrogen isotope composition of water vapor at a very tall tower (185 m) in the upper Midwest, United States, to diagnose the sources, transport, and fractionation of water vapor in the planetary boundary layer (PBL) over a 3-year period (2010 to 2012). These measurements represent the first set of annual water vapor isotope observations for this region. Several simple isotope models and cross-wavelet analyses were used to assess the importance of the Rayleigh distillation process, evaporation, and PBL entrainment processes on the isotope composition of water vapor. The vapor isotope composition at this tall tower site showed a large seasonal amplitude (mean monthly δ18Ov ranged from −40.2 to −15.9 ‰ and δ2Hv ranged from −278.7 to −113.0 ‰) and followed the familiar Rayleigh distillation relation with water vapor mixing ratio when considering the entire hourly data set. However, this relation was strongly modulated by evaporation and PBL entrainment processes at timescales ranging from hours to several days. The wavelet coherence spectra indicate that the oxygen isotope ratio and the deuterium excess (dv) of water vapor are sensitive to synoptic and PBL processes. According to the phase of the coherence analyses, we show that evaporation often leads changes in dv, confirming that it is a potential tracer of regional evaporation. Isotope mixing models indicate that on average about 31 % of the growing season PBL water vapor is derived from regional evaporation. However, isoforcing calculations and mixing model analyses for high PBL water vapor mixing ratio events ( >  25 mmol mol−1) indicate that regional evaporation can account for 40 to 60 % of the PBL water vapor. These estimates are in relatively good agreement with that derived from numerical weather model simulations. This relatively large fraction of evaporation-derived water vapor implies that evaporation has an important impact on the precipitation recycling ratio within the region. Based on multiple constraints, we estimate that the summer season recycling fraction is about 30 %, indicating a potentially important link with convective precipitation.

The response of highly productive croplands at northern mid-latitudes to climate change is a primary source of uncertainty in the global carbon cycle, and a concern for future food production. We present a decadal time series (2007 to 2019) of hourly CO 2 concentration measured at a very tall tower in the United States Corn Belt. Analyses of this record, with other long-term data in the region, reveal that warming has had a positive impact on net CO 2 uptake during the early crop growth stage, but has reduced net CO 2 uptake in both croplands and natural ecosystems during the peak growing season. Future increase in summer temperature is projected to reduce annual CO 2 sequestration in the Corn Belt by 10–20%. These findings highlight the dynamic control of warming on cropland CO 2 exchange and crop yields and challenge the paradigm that warming will continue to favor CO 2 sequestration in northern mid-latitude ecosystems.

N₂O is an important greenhouse gas and the primary stratospheric ozone depleting substance. Its deleterious effects on the environment have prompted appeals to regulate emissions from agriculture, which represents the primary anthropogenic source in the global N₂O budget. Successful implementation of mitigation strategies requires robust bottom-up inventories that are based on emission factors (EFs), simulation models, or a combination of the two. Top-down emission estimates, based on tall-tower and aircraft observations, indicate that bottom-up inventories severely underestimate regional and continental scale N₂O emissions, implying that EFs may be biased low. Here, we measured N₂O emissions from streams within the US Corn Belt using a chamber-based approach and analyzed the data as a function of Strahler stream order (S). N₂O fluxes from headwater streams often exceeded 29 nmol N₂O-N m⁻²·s⁻¹ and decreased exponentially as a function of S. This relation was used to scale up riverine emissions and to assess the differences between bottom-up and top-down emission inventories at the local to regional scale. We found that the Intergovernmental Panel on Climate Change (IPCC) indirect EF for rivers (EF5r) is underestimated up to ninefold in southern Minnesota, which translates to a total tier 1 agricultural underestimation of N₂O emissions by 40%. We show that accounting for zero-order streams as potential N₂O hotspots can more than double the agricultural budget. Applying the same analysis to the US Corn Belt demonstrates that the IPCC EF5runderestimation explains the large differences observed between top-down and bottom-up emission estimates.