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Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

 

Thermoelectric active cooling uses nontraditional thermoelectric materials with high thermal conductivity, high thermoelectric power factor, and relatively low figure of merit (ZT) to transfer large heat flows from a hot object to a cold heat sink. However, prior studies have not considered the influence of external thermal resistances associated with the heat sinks or contacts, making it difficult to design active cooling thermal systems or compare the use of low-ZT and high-ZT materials. Here, we perform a non-dimensionalized analysis of thermoelectric active cooling under forced heat flow boundary conditions, including arbitrary external thermal resistances. We identify the optimal electrical currents to minimize the heat source temperature and find the crossover heat flows at which low-ZT active cooling leads to lower source temperatures than high-ZT and even ZT→+∞ thermoelectric refrigeration. These optimal parameters are insensitive to the thermal resistance between the heat source and thermoelectric materials, but depend strongly on the heat sink thermal resistance. Finally, we map the boundaries where active cooling yields lower source temperatures than thermoelectric refrigeration. For currently considered active cooling materials, active cooling with ZT < 0.1 is advantageous compared to ZT→+∞ refrigeration for dimensionless heat sink thermal conductances larger than 15 and dimensionless source powers between 1 and 100. Thus, our results motivate further investigation of system-level thermoelectric active cooling for applications in electronics thermal management. 

Nitrous oxide (N₂O) is a greenhouse gas and stratospheric ozone‐depleting substance with large and growing anthropogenic emissions. Previous studies identified the influx of N₂O‐depleted air from the stratosphere to partly cause the seasonality in tropospheric N₂O (aN₂O), but other contributions remain unclear. Here, we combine surface fluxes from eight land and four ocean models from phase 2 of the Nitrogen/N₂O Model Intercomparison Project with tropospheric transport modeling to simulate aN₂O at eight remote air sampling sites for modern and pre‐industrial periods. Models show general agreement on the seasonal phasing of zonal‐average N₂O fluxes for most sites, but seasonal peak‐to‐peak amplitudes differ several‐fold across models. The modeled seasonal amplitude of surface aN₂O ranges from 0.25 to 0.80 ppb (interquartile ranges 21%–52% of median) for land, 0.14–0.25 ppb (17%–68%) for ocean, and 0.28–0.77 ppb (23%–52%) for combined flux contributions. The observed seasonal amplitude ranges from 0.34 to 1.08 ppb for these sites. The stratospheric contributions to aN₂O, inferred by the difference between the surface‐troposphere model and observations, show 16%–126% larger amplitudes and minima delayed by ∼1 month compared to Northern Hemisphere site observations. Land fluxes and their seasonal amplitude have increased since the pre‐industrial era and are projected to grow further under anthropogenic activities. Our results demonstrate the increasing importance of land fluxes for aN₂O seasonality. Considering the large model spread, in situ aN₂O observations and atmospheric transport‐chemistry models will provide opportunities for constraining terrestrial and oceanic biosphere models, critical for projecting carbon‐nitrogen cycles under ongoing global warming.

 

Wetlands are the largest emitters of biogenic methane (CH₄) and represent the highest source of uncertainty in global CH₄budgets. Here, we aim to improve the realism of wetland representation in the U.S. Department of Energy's Exascale Earth System Model land surface model, ELM, thereby reducing uncertainty of CH₄flux predictions. We develop an updated version, ELM‐Wet, where we activate a separate landunit for wetlands that handles multiple wetland‐specific eco‐hydrological patch functional types. We introduce more realistic hydrological forcing through prescribing site‐level constraints on surface water elevation, which allows resolving different sustained inundation depth for different patches, and if data exists, prescribing inundation depth. We modified the calculation of aerenchyma transport diffusivity based on observed conductance per leaf area for different vegetation types. We use Bayesian Optimization to parameterize CO₂and CH₄fluxes in the developed wet‐landunit. Site‐level simulations of a coastal non‐tidal freshwater wetland in Louisiana were performed with the updated model. Eddy covariance observations of CO₂and CH₄fluxes from 2012 to 2013 were used to train the model and data from 2021 were used for validation. Patch‐specific chamber flux observations and observations of CH₄concentration profiles in the soil porewater from 2021 were used for evaluation of the model performance. Our results show that ELM‐Wet reduces the model's CH₄emission root mean squared error by up to 33% and is able to represent inter‐daily CO₂and CH₄flux variability across the wetland's eco‐hydrological patches, including during periods of extreme dry or wet conditions.

 

Soil organic matter decomposition and its interactions with climate depend on whether the organic matter is associated with soil minerals. However, data limitations have hindered global-scale analyses of mineral-associated and particulate soil organic carbon pools and their benchmarking in Earth system models used to estimate carbon cycle–climate feedbacks. Here we analyse observationally derived global estimates of soil carbon pools to quantify their relative proportions and compute their climatological temperature sensitivities as the decline in carbon with increasing temperature. We find that the climatological temperature sensitivity of particulate carbon is on average 28% higher than that of mineral-associated carbon, and up to 53% higher in cool climates. Moreover, the distribution of carbon between these underlying soil carbon pools drives the emergent climatological temperature sensitivity of bulk soil carbon stocks. However, global models vary widely in their predictions of soil carbon pool distributions. We show that the global proportion of model pools that are conceptually similar to mineral-protected carbon ranges from 16 to 85% across Earth system models from the Coupled Model Intercomparison Project Phase 6 and offline land models, with implications for bulk soil carbon ages and ecosystem responsiveness. To improve projections of carbon cycle–climate feedbacks, it is imperative to assess underlying soil carbon pools to accurately predict the distribution and vulnerability of soil carbon.

 

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 CH₄fluxes (FCH₄) at different time scales. A Monte Carlo approach was developed to incorporate flux observation error to avoid misidentification of the time scales that dominate model error. Our results suggest that (a) significant model‐observation disagreements are mainly at multi‐day time scales (<15 days); (b) most of the models can capture the CH₄variability at monthly and seasonal time scales (>32 days) for the boreal and Arctic tundra wetland sites but have significant bias in variability at seasonal time scales for temperate and tropical/subtropical sites; (c) model errors exhibit increasing power spectrum as time scale increases, indicating that biases at time scales <5 days could contribute to persistent systematic biases on longer time scales; and (d) differences in error pattern are related to model structure (e.g., proxy of CH₄production). Our evaluation suggests the need to accurately replicate FCH₄variability, especially at short time scales, in future wetland CH₄model developments.

 

Earth System Models (ESMs) have implemented nitrogen (N) cycles to account for N limitation on terrestrial carbon uptake. However, representing inputs, losses and recycling of N in ESMs is challenging. Here, we use global rates and ratios of key soil N fluxes, including nitrification, denitrification, mineralization, leaching, immobilization and plant uptake (both NH4+ and NO3-), from the literature to evaluate the N cycles in the land model components of two ESMs. The two land models evaluated here, ELMv1-ECA and CLM5.0, originated from a common model but have diverged in their representation of plant/microbe competition for soil N. The models predict similar global rates of gross primary productivity (GPP) but have ~2 to 3-fold differences in their underlying global mineralization, immobilization, plant N uptake, nitrification and denitrification fluxes. Both models dramatically underestimate the immobilization of NO3- by soil bacteria compared to literature values and predict dominance of plant uptake by a single form of mineral nitrogen (NO3- for ELM, with regional exceptions, and NH4+ for CLM5.0). CLM5.0 strongly underestimates the global ratio of gross nitrification:gross mineralization and both models likely substantially underestimate the ratio of nitrification:denitrification. Few experimental data exist to evaluate this last ratio, in part because nitrification and denitrification are quantified with different techniques and because denitrification fluxes are difficult to measure at all. More observational constraints on soil nitrogen fluxes like nitrification and denitrification, as well as greater scrutiny of the functional impact of introducing separate NH4+ and NO3- pools into ESMs, could help improve confidence in present and future simulations of N limitation on the carbon cycle. 

The radiance of sky brightness differs principally with wavelength passband. Atmospheric scattering of sunlight causes the radiation in the near-infrared band. The Antarctic is a singular area of the planet, marked by an unparalleled climate and geographical conditions, including the coldest temperatures and driest climate on Earth, which leads it to be the best candidate site for observing in infrared bands. At present, there are still no measurements of night-sky brightness at DOME A. We have developed the Near-Infrared Sky Brightness Monitor (NISBM) in the J, H, and Ks bands for measurements at DOME A. The instruments were installed at DOME A in 2019 and early results of NIR sky brightness from 2019 January–April have been obtained. The variation of sky background brightness with solar elevation and scanning angle is analysed. The zenith sky flux intensity for the early night at DOME A in the J band is in the 600–1100 μJy arcsec−2 range, that in the H band is between 1100 and 2600 μJy arcsec−2, and that in the Ks band is in the range ∼200–900 μJy arcsec−2. This result shows that the sky brightness in J and H bands is close to that of Ali in China and Mauna Kea in the USA. The sky brightness in the Ks band is much better than that in Ali, China and Mauna Kea, USA. This shows that, from our early results, DOME A is a good site for astronomical observation in the Ks band.