Improve Model Parameterization Research Articles

The MOSAiC Distributed Network: Observing the coupled Arctic system with multidisciplinary, coordinated platforms

Central Arctic properties and processes are important to the regional and global coupled climate system. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Distributed Network (DN) of autonomous ice-tethered systems aimed to bridge gaps in our understanding of temporal and spatial scales, in particular with respect to the resolution of Earth system models. By characterizing variability around local measurements made at a Central Observatory, the DN covers both the coupled system interactions involving the ocean-ice-atmosphere interfaces as well as three-dimensional processes in the ocean, sea ice, and atmosphere. The more than 200 autonomous instruments (“buoys”) were of varying complexity and set up at different sites mostly within 50 km of the Central Observatory. During an exemplary midwinter month, the DN observations captured the spatial variability of atmospheric processes on sub-monthly time scales, but less so for monthly means. They show significant variability in snow depth and ice thickness, and provide a temporally and spatially resolved characterization of ice motion and deformation, showing coherency at the DN scale but less at smaller spatial scales. Ocean data show the background gradient across the DN as well as spatially dependent time variability due to local mixed layer sub-mesoscale and mesoscale processes, influenced by a variable ice cover. The second case (May–June 2020) illustrates the utility of the DN during the absence of manually obtained data by providing continuity of physical and biological observations during this key transitional period. We show examples of synergies between the extensive MOSAiC remote sensing observations and numerical modeling, such as estimating the skill of ice drift forecasts and evaluating coupled system modeling. The MOSAiC DN has been proven to enable analysis of local to mesoscale processes in the coupled atmosphere-ice-ocean system and has the potential to improve model parameterizations of important, unresolved processes in the future.

Spatial and Temporal Variation of Subseasonal-to-Seasonal (S2S) Precipitation Reforecast Skill across the CONUS

Abstract Precipitation forecasts, particularly at subseasonal-to-seasonal (S2S) time scale, are essential for informed and proactive water resource management. Although S2S precipitation forecasts have been evaluated, no systematic decomposition of the skill, Nash–Sutcliffe efficiency (NSE) coefficient, has been analyzed toward understanding the forecast accuracy. We decompose the NSE of S2S precipitation forecast into its three components—correlation, conditional bias, and unconditional bias—by four seasons, three lead times (1–12, 1–22, and 1–32 days), and three models, European Centre of Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction’s (NCEP) Climate Forecast System (CFS) model, and Environment and Climate Change Canada (ECCC), over the conterminous United States (CONUS). Application of a dry threshold, removal of grid cells with seasonal climatological precipitation means below 0.01 in. per day, is important as the NSE and correlations are lower across all seasons after masking areas with low precipitation values. Further, a west-to-east gradient in S2S forecast skill exists, and forecast skill was better during the winter months and for areas closer to the coast. Overall, ECMWF’s model performance was stronger than both ECCC and NCEP CFS’s performance, mainly for the forecasts issued during the fall and winter months. However, ECCC and NCEP CFS performed better for the forecast issued during the spring months and for areas further from the coast. Postprocessing using simple model output statistics could reduce both unconditional and conditional biases to zero, thereby offering better skill for regimes with high correlation. Our decomposition results show that efforts should focus on improving model parameterization and initialization schemes for climate regimes with low correlation.

Improving parameterization of an evapotranspiration estimation model with eddy covariance measurements for a regional irrigation scheduling program

Actual evapotranspiration (ETa) is an essential variable in linking energy cycles, carbon, and water, yet challenging to measure. Inputs uncertainty and deficiencies in the key elements of hydrologic models are fundamental challenges for optimizing model performance. Furthermore, the performance of land surface model-based ETa, reanalysis, and remote sensing products varies with spatiotemporal scales. Here, we evaluate sources of bias in the regional Wisconsin Irrigation and Scheduling Program (WISP) model and develop a correction using eddy covariance (EC) observations. ETa, observations were made for five years (2018–2022) using EC systems in agricultural fields in Wisconsin. WISP model ETa bias was linked to underestimation of net longwave radiation (LWnet) that was traced to incorrect specification of effective clear sky atmospheric emissivity (εa,clr). Applying a correction to the εa,clr led to reduced WISP model percent bias (pbias) and error for both LWnet and ETa. The calibrated model more accurately represented observed ETa. The results indicate that explicit treatment of the LWnet balance decreases the uncertainty of model parameters and improves the WISP model performance at independent sites. Applying this improved model parameterization reduced the bias of LWnet radiation from 62.8% to -6.2%, which improved the Nash-Sutcliffe Efficiency (NSE) from -0.08 to 0.52 for ETa on training sites. Additionally, overall pbias was significantly reduced (p = 0.035) for validation sites after WISP correction. Hence, WISP performance improved for different crop types when optimal regional parameters were used, confirming the physical parameters' reliability. Our results highlight that model development should focus on energy balance parameterizations to improve ET simulation and the accuracy of hydrologic and climatic simulations for understanding critical processes underlying hydrologic and climatic variability and change over land.

Seismic differential semblance-oriented migration velocity analysis — Status and the way forward

Differential semblance optimization (DSO) is a seismic waveform inversion approach. It has been developed to remove the limitation of conventional full-waveform inversion as a velocity model building tool when only reflected waves are available. It has connections with migration velocity analysis (MVA) techniques defined in the image domain to build macrovelocity models, with a split between the macromodel and the small-scale heterogeneities. Among the various DSO approaches, DSO-MVA has unique characteristics for velocity model building of complex structures, while ensuring the convergence to the global minimum in a local optimization approach: the wavefields are computed with the two-way wave equation operators, focusing panels are built in a survey-sinking mode, and the quality of the macrovelocity model is evaluated on common-image gathers in the subsurface-offset domain through a differential annihilator. We review the significant aspects that have been better understood over the past 15 years and discuss the way forward. The first identified issue has been the imprint of the small-scale heterogeneities on the macromodel update that is revealed with the use of wave equation-based Green’s functions and an ad hoc solution is subsequently developed by slightly modifying the definition of the objective function. A second important aspect has been the development of efficient approaches to compute the required quantitative migration based on approximate inverses to replace the previously used migrations. The issue of correctly interpreting the amplitudes has also moved into one of an improved model parameterization, i.e., from constant-density acoustics media to full acoustic media as a path toward fully viscoelastic media. Finally, we discuss the way forward with the challenges of extending DSO-MVA beyond the single scattering approximation, for processing diving waves and multiples, and also the challenge of numerical implementation, which remains critical even in two dimensions, for applicability to large-scale real data.

Trophic Modelling of the Cross River Estuary Ecosystem, Nigeria

A trophic modelling of the Cross River estuary ecosystem was undertaken using Ecopath with Ecosim (EwE). Data were obtained from peer reviewed journals, monographs and stock assessment reports and used to construct the Ecopath model used to investigate the structure, function and system throughput of the estuary. These data included biological data such as biomass, production/ biomass, consumption/biomass, fish catch and diet composition of each species or functional group included in the model. The network analysis routine was used to estimate trophic levels and trophic interactions of functional groups, health condition and developmental stage, and food web features. Results of Ecopath model revealed three apical predators: Shark, Senegalese tonguesole, and Sea catfish. The ratio of total primary production to total respiration value of 4.182 revealed that the Cross River estuary is an immature ecosystem void of biological and ecosystem pollution and is at its early developmental stage. The mean trophic level of the catch of 2.186 for the system and the high gross food conversion efficiency (GE) of 0.300 for Macrobrachium spp and 0.292 for Penaeid shrimp, revealed high fishing pressure on the herbivores. The estuary ecosystem has a short food chain and low connectance index of 0.1879 which has phytoplankton being grazed upon by zooplankton which in turn are taken up by bonga. Model results and physico-chemical parameters showed that the Cross River estuary is still relatively pristine and immature ecosystem, void of organic pollution and with relatively low primary productivity as evident in relatively low nutrient concentration. The high value of ecotrophic efficiency (EE) of 0.993 for bonga and 0.991 for Macrobrachium spp indicate that the population of these animals are heavily exploited and predated upon in the estuary. Despite that this is a preliminary trophic model, it is clear that fish populations in the estuary are experiencing excessive fishing pressure; thus necessitating management measures to reduce pressure, while relevant data especially on fish diet composition and fish landing should be collected in order to get improved model parameterization in the future.

Global net biome CO2 exchange predicted comparably well using parameter-environment relationships and plant functional types.

Accurate estimation and forecasts of net biome CO2 exchange (NBE) are vital for understanding the role of terrestrial ecosystems in a changing climate. Prior efforts to improve NBE predictions have predominantly focused on increasing models' structural realism (and thus complexity), but parametric error and uncertainty are also key determinants of model skill. Here, we investigate how different parameterization assumptions propagate into NBE prediction errors across the globe, pitting the traditional plant functional type (PFT)-based approach against a novel top-down, machine learning-based "environmental filtering" (EF) approach. To do so, we simulate these contrasting methods for parameter assignment within a flexible model-data fusion framework of the terrestrial carbon cycle (CARDAMOM) at a global scale. In the PFT-based approach, model parameters from a small number of select locations are applied uniformly within regions sharing similar land cover characteristics. In the EF-based approach, a pixel's parameters are predicted based on underlying relationships with climate, soil, and canopy properties. To isolate the role of parametric from structural uncertainty in our analysis, we benchmark the resulting PFT-based and EF-based NBE predictions with estimates from CARDAMOM's Bayesian optimization approach (whereby "true" parameters consistent with a suite of data constraints are retrieved on a pixel-by-pixel basis). When considering the mean absolute error of NBE predictions across time, we find that the EF-based approach matches or outperforms the PFT-based approach at 55% of pixels-a narrow majority. However, NBE estimates from the EF-based approach are susceptible to compensation between errors in component flux predictions and predicted parameters can align poorly with the assumed "true" values. Overall, though, the EF-based approach is comparable to conventional approaches and merits further investigation to better understand and resolve these limitations. This work provides insight into the relationship between terrestrial biosphere model performance and parametric uncertainty, informing efforts to improve model parameterization via PFT-free and trait-based approaches.

Xin’anjiang Nested Experimental Watershed (XAJ-NEW) for Understanding Multiscale Water Cycle: Scientific Objectives and Experimental Design

• Designed a five-tier nested experimental watershed for multi-scale water cycle study. • Hydrological processes exhibit a spatiotemporal scaling effect. • Canopy leaf density has an exponential effect on rainfall interception. • Surface runoff serves as a major component of total runoff in the humid zone. This paper presents the background, scientific objectives, experimental design, and preliminary achievements of the Xin’anjiang nested experimental watershed (XAJ-NEW), implemented in 2017 in eastern China, which has a subtropical humid monsoon climate and a total area of 2674 km 2 . The scientific objectives of the XAJ-NEW include building a comprehensive, multiscale, and nested hydrometeorological monitoring and experimental program, strengthening the observation of the water cycle, discovering the spatiotemporal scaling effects of hydrological processes, and revealing the mechanisms controlling runoff generation and partitioning in a typical humid, hilly area. After two years of operation, preliminary results indicated scale-dependent variability in key hydrometeorological processes and variables such as precipitation, runoff, groundwater, and soil moisture. The effects of canopy interception and runoff partitioning between the surface and subsurface were also identified. Continuous operation of this program can further reveal the mechanisms controlling runoff generation and partitioning, discover the spatiotemporal scaling effects of hydrological processes, and understand the impacts of climate change on hydrological processes. These findings provide new insights into understanding multiscale hydrological processes and their responses to meteorological forcings, improving model parameterization schemes, and enhancing weather and climate forecast skills.

Microphysical characteristics of seasonal rainfall observed by a Parsivel disdrometer in the Tianshan Mountains, China

Raindrop size distribution (DSD) is critical for understanding rainfall microphysical processes. Correlated DSD relationships, including the radar reflectivity factor-rainfall rate (Z–R), shape factor-slope parameter (μ–∧), and the rainfall kinetic energy-R (mass-weighted average diameter) (KE–R (Dm)) are significant in many fields such as quantitative precipitation estimation, parameterization schemes in weather forecast models, and assessing rainfall kinetic energy; however, the collective scientific understanding of DSD in arid regions remains insufficient. Here, 2020–2021 DSD data from the Tianshan Mountains, China, were collected to assess various corresponding characteristics for a typical arid region in China across different seasons, rainfall types, and rainfall rate classes. Rainfall in the fall (summer) had the most (least) small raindrops (diameter < 1 mm), the least (most) mid-size raindrops (1 ≤ diameter ≤ 3 mm), and large raindrops (diameter > 3 mm). Mean Dm was the largest (smallest) in the summer (fall) at 1.059 mm (0.876 mm), while the mean generalized intercept parameter was the largest (smallest) in the fall (winter) at 3.738 (3.505). For stratiform rainfall, fall (summer) had the most (least) small raindrops, as well as the least (most) mid-size and large raindrops, whereas, for convective rainfall, summer displayed fewer small and mid-size raindrops compared to spring. Regarding the rainfall rate classes, the concentration of mid- and large-size raindrops in spring changed from a minimum at C1 (0.1 ≤ R < 0.5 mm·h−1; notably comparable to fall rates) to C4 (2 ≤ R < 5 mm·h−1; comparable to summer), before peaking beyond summer rates at C5 (R ≥ 5 mm·h−1). The localized Z-R, μ–∧, KEtime–R, and KEmm–Dm relationships also exhibited marked seasonal characteristics, which ultimately impact estimates of regional radar quantitative precipitation and rainfall kinetic energy, as well as efforts aimed at improving model parameterization schemes. Additionally, the more abundant water vapor, the more frequent cold rain process, and the stronger warm–dry atmospheric vertical environment in summer appeared as the most important influencing factors leading to the fewest small-, and greatest mid- and large-size raindrops.

Initial pH Governs Secondary Organic Aerosol Phase State and Morphology after Uptake of Isoprene Epoxydiols (IEPOX).

Aerosol acidity increases secondary organic aerosol (SOA) formed from the reactive uptake of isoprene-derived epoxydiols (IEPOX) by enhancing condensed-phase reactions within sulfate-containing submicron particles, leading to low-volatility organic products. However, the link between the initial aerosol acidity and the resulting physicochemical properties of IEPOX-derived SOA remains uncertain. Herein, we show distinct differences in the morphology, phase state, and chemical composition of individual organic-inorganic mixed particles after IEPOX uptake to ammonium sulfate particles with different initial atmospherically relevant acidities (pH = 1, 3, and 5). Physicochemical properties were characterized via atomic force microscopy coupled with photothermal infrared spectroscopy (AFM-PTIR) and Raman microspectroscopy. Compared to less acidic particles (pH 3 and 5), reactive uptake of IEPOX to the most acidic particles (pH 1) resulted in 50% more organosulfate formation, clearer phase separation (core-shell), and more irregularly shaped morphologies, suggesting that the organic phase transitioned to semisolid or solid. This study highlights that initial aerosol acidity may govern the subsequent aerosol physicochemical properties, such as viscosity and morphology, following the multiphase chemical reactions of IEPOX. These results can be used in future studies to improve model parameterizations of SOA formation from IEPOX and its properties, toward the goal of bridging predictions and atmospheric observations.

Uncertain response of ocean biological carbon export in a changing world

The transfer of organic carbon from the upper to the deep ocean by particulate export flux is the starting point for the long-term storage of photosynthetically fixed carbon. This ‘biological carbon pump’ is a critical component of the global carbon cycle, reducing atmospheric CO2 levels by ~200 ppm relative to a world without export flux. This carbon flux also fuels the productivity of the mesopelagic zone, including important fisheries. Here we show that, despite its importance for understanding future ocean carbon cycling, Earth system models disagree on the projected response of the global export flux to climate change, with estimates ranging from −41% to +1.8%. Fundamental constraints to understanding export flux arise because a myriad of interconnected processes make the biological carbon pump challenging to both observe and model. Our synthesis prioritizes the processes likely to be most important to include in modern-day estimates (particle fragmentation and zooplankton vertical migration) and future projections (phytoplankton and particle size spectra and temperature-dependent remineralization) of export. We also identify the observations required to achieve more robust characterization, and hence improved model parameterization, of export flux and thus reduce uncertainties in current and future estimates in the overall cycling of carbon in the ocean. A synthesis of recent work on marine carbon export fluxes finds that many processes that are key to understanding the effects of a warming climate on ocean carbon cycling are missing from current climate models.

Turbulence within Rain-Formed Fresh Lenses during the SPURS-2 Experiment

AbstractObservations of salinity, temperature, and turbulent dissipation rate were made in the top meter of the ocean using the ship-towed Surface Salinity Profiler as part of the second Salinity Processes in the Upper Ocean Regional Study (SPURS-2) to assess the relationships between wind, rain, near-surface stratification, and turbulence. A wide range of wind and rain conditions were observed in the eastern tropical Pacific Ocean near 10°N, 125°W in summer–autumn 2016 and 2017. Wind was the primary driver of near-surface turbulence and the mixing of rain-formed fresh lenses, with lenses generally persisting for hours when wind speeds were under 5 m s−1 and mixing away immediately at higher wind speeds. Rain influenced near-surface turbulence primarily through stratification. Near-surface stratification caused by rainfall or diurnal warming suppressed deeper turbulent dissipation rates when wind speeds were under 3 m s−1. In one case with 4–5 m s−1 winds, rain-induced stratification enhanced dissipation rates within the stratified layer. At wind speeds above 7–8 m s−1, strong stratification was not observed in the upper meter during rain, indicating that rain lenses do not form at wind speeds above 8 m s−1. Raindrop impacts enhanced turbulent dissipation rates at these high wind speeds in the absence of near-surface stratification. Measurements of air–sea buoyancy flux, wind speed, and near-surface turbulence can be used to predict the presence of stratified layers. These findings could be used to improve model parameterizations of air–sea interactions and, ultimately, our understanding of the global water cycle.

Global offshore wind energy resources using the new ERA-5 reanalysis

A global quantitative characterization of offshore wind power density is presented over the economic exclusive zones (EEZs), at annual and seasonal scales, based on the recently released ECMWF ERA-5 reanalysis. Compared to its predecessors, ERA-5 features increased spatial and temporal resolutions, along with improved model parameterizations and data assimilation methodologies. Consequently, the present study represents an updated benchmark of wind resources over the global oceans. Further robustness to this argument is provided by our results showing that of the EEZs with large wind energy potential are characterized by regional-scale circulation features (coastal jets and breezes, land-sea and topographic circulations), known require high model resolution to be accurately simulated. Additionally, it is demonstrated that larger errors (up to 50%) can result from estimating wind power density from logarithmically extrapolated near-surface wind speed when compared to using high-resolution model level information. Coarser (daily) temporal resolution is also found to cause widespread increases in offshore wind power density errors between 10% and 30% compared to higher (hourly) resolution data. Further leveraging on the high vertical resolution of ERA-5, we show the large benefit of future increases in hub-heights from 100 m to 250 m for global offshore wind resources. Such technological advance results in widespread gains (ranging between +5% and +50%) throughout the global EEZs and no losses for all seasons. Moreover, the gains are >+10% for all regions with large wind energy potential at 100 m and >+40% over the Arctic Pacific sector and in regions characterized by the presence of coastal low-level jets.

Hysteretic relationship between plant productivity and methane uptake in an alpine meadow

The relationship between methane (CH4) and gross primary productivity (GPP) remains elusive and the underlying mechanism is even far from clear. To explore this relationship, we continually monitored CH4 flux and CO2 flux by eddy covariance technique in an alpine meadow on the Qinghai–Tibetan Plateau, China, during June 2015–December 2017. We found a parabolic-like relationship of net CH4 flux with GPP change in both the morning and afternoon at diurnal scale. However, with similar GPP, CH4 uptake was stronger in the morning than that in the afternoon, forming an anti-clockwise hysteretic relationship between CH4 and GPP in all the three years. Meanwhile, similar hysteresis loop was also observed between CH4 flux and photosynthetically active radiation (PAR), and between PAR and air temperature (Ta) during all the three years, with stronger CH4 uptake (or lower Ta) in the morning than that in the afternoon at the same PAR. Results from our study suggested that the hysteretic relationship between CH4 flux and GPP might be mainly induced by the hysteresis between PAR and Ta. The similar hysteresis pattern between CH4 flux (or Ta) and PAR in the non-growing season further confirmed our hypothesis. The findings provide new insights into the coupling of plant photosynthetic activity and net CH4 flux and help to improve model parameterization for simulating CO2–CH4 interaction processes.

The response of stomatal conductance to seasonal drought in tropical forests.

Stomata regulate CO2 uptake for photosynthesis and water loss through transpiration. The approaches used to represent stomatal conductance (gs ) in models vary. In particular, current understanding of drivers of the variation in a key parameter in those models, the slope parameter (i.e. a measure of intrinsic plant water-use-efficiency), is still limited, particularly in the tropics. Here we collected diurnal measurements of leaf gas exchange and leaf water potential (Ψleaf ), and a suite of plant traits from the upper canopy of 15 tropical trees in two contrasting Panamanian forests throughout the dry season of the 2016 El Niño. The plant traits included wood density, leaf-mass-per-area (LMA), leaf carboxylation capacity (Vc,max ), leaf water content, the degree of isohydry, and predawn Ψleaf . We first investigated how the choice of four commonly used leaf-level gs models with and without the inclusion of Ψleaf as an additional predictor variable influence the ability to predict gs , and then explored the abiotic (i.e. month, site-month interaction) and biotic (i.e. tree-species-specific characteristics) drivers of slope parameter variation. Our results show that the inclusion of Ψleaf did not improve model performance and that the models that represent the response of gs to vapor pressure deficit performed better than corresponding models that respond to relative humidity. Within each gs model, we found large variation in the slope parameter, and this variation was attributable to the biotic driver, rather than abiotic drivers. We further investigated potential relationships between the slope parameter and the six available plant traits mentioned above, and found that only one trait, LMA, had a significant correlation with the slope parameter (R2 =0.66, n=15), highlighting a potential path towards improved model parameterization. This study advances understanding of gs dynamics over seasonal drought, and identifies a practical, trait-based approach to improve modeling of carbon and water exchange in tropical forests.

Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES)

Abstract. Vegetation plays an important role in regulating global carbon cycles and is a key component of the Earth system models (ESMs) that aim to project Earth's future climate. In the last decade, the vegetation component within ESMs has witnessed great progress from simple “big-leaf” approaches to demographically structured approaches, which have a better representation of plant size, canopy structure, and disturbances. These demographically structured vegetation models typically have a large number of input parameters, and sensitivity analysis is needed to quantify the impact of each parameter on the model outputs for a better understanding of model behavior. In this study, we conducted a comprehensive sensitivity analysis to diagnose the Community Land Model coupled to the Functionally Assembled Terrestrial Simulator, or CLM4.5(FATES). Specifically, we quantified the first- and second-order sensitivities of the model parameters to outputs that represent simulated growth and mortality as well as carbon fluxes and stocks for a tropical site with an extent of 1×1∘. While the photosynthetic capacity parameter (Vc,max25) is found to be important for simulated carbon stocks and fluxes, we also show the importance of carbon storage and allometry parameters, which determine survival and growth strategies within the model. The parameter sensitivity changes with different sizes of trees and climate conditions. The results of this study highlight the importance of understanding the dynamics of the next generation of demographically enabled vegetation models within ESMs to improve model parameterization and structure for better model fidelity.

Improving Representation of Deforestation Effects on Evapotranspiration in the E3SM Land Model

AbstractEvapotranspiration (ET) plays an important role in land‐atmosphere coupling of energy, water, and carbon cycles. Following deforestation, ET is typically observed to decrease substantially as a consequence of decreases in leaf area and roots and increases in runoff. Changes in ET (latent heat flux) revise the surface energy and water budgets, which further affects large‐scale atmospheric dynamics and feeds back positively or negatively to long‐term forest sustainability. In this study, we used observations from a recent synthesis of 29 pairs of adjacent intact and deforested FLUXNET sites to improve model parameterization of stomatal characteristics, photosynthesis, and soil water dynamics in version 1 of the Energy Exascale Earth System Model (E3SM) Land Model (ELMv1). We found that default ELMv1 predicts an increase in ET after deforestation, likely leading to incorrect estimates of the effects of deforestation on land‐atmosphere coupling. The calibrated model accurately represented the FLUXNET observed deforestation effects on ET. Importantly, the search for global optimal parameters converged at values consistent with recent observational syntheses, confirming the reliability of the calibrated physical parameters. Applying this improved model parameterization to the globe scale reduced the bias of annual ET simulation by up to ~600 mm/year. Analysis on the roles of parameters suggested that future model development to improve ET simulation should focus on stomatal resistance and soil water‐related parameterizations. Finally, our predicted differences in seasonal ET changes from deforestation are large enough to substantially affect land‐atmosphere coupling and should be considered in such studies.

Diel and Seasonal Dynamics of Ecosystem‐Scale Methane Flux and Their Determinants in an Alpine Meadow

AbstractTemporal variations of methane flux (FCH4) and its underlying mechanisms still remain poorly understood. To quantify diurnal and seasonal patterns of FCH4 and investigate its determinants, we monitored FCH4 using eddy covariance in an alpine meadow on the Qinghai‐Tibetan Plateau, China, from June 2015 to December 2016. As a strong CH4 sink, the alpine meadow on the Qinghai‐Tibetan Plateau consumed 0.41 ± 0.04 Tg CH4/year. There was an obvious diurnal pattern with more CH4 uptakes during the nighttime than the daytime for both growing and nongrowing season. The diurnal FCH4 during the growing and nongrowing season were positively correlated with air temperature (Ta), volumetric water content, friction velocity (u*), and vapor pressure deficit. The growing season FCH4 showed a significant quadratic polynomial relationship with the canopy conductance (Gw) and gross primary production). FCH4 was significantly higher in the growing season than in the nongrowing season. The seasonal FCH4 was negatively correlated with soil temperature and net radiation (Rn) but not with volumetric water content and gross primary production. Ridge regression models indicated that Ta and u* explained 83% of the variation in the diel dynamics of FCH4 during the growing season and explained 72% of the variation during the nongrowing season. Rn accounted for 49% of variations of FCH4 at the seasonal scale. The temporal patterns and the environmental controlling factors revealed in this study may improve model parameterization for biosphere‐atmosphere CH4 exchange simulation as well as the methane budget estimation.

Overview of the HI-SCALE Field Campaign: A New Perspective on Shallow Convective Clouds

AbstractShallow convective clouds are common, occurring over many areas of the world, and are an important component in the atmospheric radiation budget. In addition to synoptic and mesoscale meteorological conditions, land–atmosphere interactions and aerosol–radiation–cloud interactions can influence the formation of shallow clouds and their properties. These processes exhibit large spatial and temporal variability and occur at the subgrid scale for all current climate, operational forecast, and cloud-system-resolving models; therefore, they must be represented by parameterizations. Uncertainties in shallow cloud parameterization predictions arise from many sources, including insufficient coincident data needed to adequately represent the coupling of cloud macrophysical and microphysical properties with inhomogeneity in the surface-layer, boundary layer, and aerosol properties. Predictions of the transition of shallow to deep convection and the onset of precipitation are also affected by errors in simulated shallow clouds. Coincident data are a key factor needed to achieve a more complete understanding of the life cycle of shallow convective clouds and to develop improved model parameterizations. To address these issues, the Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems (HI-SCALE) campaign was conducted near the Atmospheric Radiation Measurement (ARM) Southern Great Plains site in north-central Oklahoma during the spring and summer of 2016. We describe the scientific objectives of HI-SCALE as well as the experimental approach, overall weather conditions during the campaign, and preliminary findings from the measurements. Finally, we discuss scientific gaps in our understanding of shallow clouds that can be addressed by analysis and modeling studies that use HI-SCALE data.

Atmosphere-terrestrial exchange of gaseous elemental mercury: parameterization improvement through direct comparison with measured ecosystem fluxes.

To simulate global mercury (Hg) dynamics in chemical transport models (CTMs), surface-atmosphere exchange of gaseous elemental mercury, Hg0, is often parameterized based on resistance-based dry deposition schemes coupled with a re-emission function, mainly from soils. Despite extensive use of this approach, direct evaluations of this implementation against field observations of net Hg0 exchange are lacking. In this study, we evaluate an existing net exchange parameterization (referred to here as the base model) by comparing modeled fluxes of Hg0 to fluxes measured in the field using micrometeorological techniques. Comparisons were performed in two terrestrial ecosystems: a grassland site in Switzerland and an Arctic tundra site in Alaska, U.S., each including summer and winter seasons. The base model included the dry deposition and soil re-emission parameterizations from Zhang et al. (2003) and the global CTM GEOS-Chem, respectively. Comparisons of modeled and measured Hg0 fluxes showed large discrepancies, particularly in the summer months when the base model overestimated daytime net deposition by approximately 9 and 2 ng m-2 h-1 at the grassland and tundra sites, respectively. In addition, the base model was unable to capture a measured nighttime net Hg0 deposition and wintertime deposition. We conducted a series of sensitivity analyses and recommend that Hg simulations using CTMs: (i) reduce stomatal uptake of Hg0 over grassland and tundra in models by a factor 5-7; (ii) increase nighttime net Hg0 deposition, e.g., by increasing ground and cuticular uptake by reducing the respective resistance terms by factors of 3-4 and 2-4, respectively; and (iii) implement a new soil re-emission parameterization to produce larger daytime emissions and lower nighttime emissions. We also compared leaf Hg0 uptake over the growing season estimated by the dry deposition model against foliar Hg measurements, which revealed good agreement with the measured leaf Hg concentrations after adjusting the base model as suggested above. We conclude that the use of resistance-based models combined with the new soil re-emission flux parameterization is able to reproduce observed diel and seasonal patterns of Hg0 exchange in these ecosystems. This approach can be used to improve model parameterizations for other ecosystems if flux measurements become available.

Moisture pulse-reserve in the soil-plant continuum observed across biomes.

The degree to which individual pulses of available water drive plant activity across diverse biomes and climates is not well understood. It has previously only been investigated in a few dryland locations. Here, plant water uptake following pulses of surface soil moisture, an indicator for the pulse-reserve hypothesis, is investigated across South America, Africa and Australia with satellite-based estimates of surface soil and canopy water content. Our findings show that this behaviour is widespread: occurring over half of the vegetated landscapes. We estimate spatially varying soil moisture thresholds at which plant water uptake ceases, noting dependence on soil texture and proximity to the wilting point. The soil type and biome-dependent soil moisture threshold and the plant soil water uptake patterns at the scale of Earth system models allow a unique opportunity to test and improve model parameterization of vegetation function under water limitation.