Journal of Advances in Modeling Earth Systems最新文献

{"title":"Impact of Turbulence Representation on the Relationship Between Cloud Feedback and Aerosol-Cloud Interaction in an E3SMv2 Perturbed Parameter Ensemble","authors":"Yi Qin,&nbsp;Po-Lun Ma,&nbsp;Mark D. Zelinka,&nbsp;Stephen A. Klein,&nbsp;Tao Zhang,&nbsp;Xue Zheng,&nbsp;Vincent E. Larson,&nbsp;Meng Huang","doi":"10.1029/2024MS004756","DOIUrl":"https://doi.org/10.1029/2024MS004756","url":null,"abstract":"<p>Recent studies reveal an anti-correlation between global cloud feedback (CF) and effective radiative forcing due to aerosol-cloud interaction (ERFaci) in Earth system models, but the physical mechanisms underlying it remain uncertain. Here we investigate how different turbulence representations contribute to this relationship over the global ocean using an ensemble of Energy Exascale Earth System Model version 2 simulations with perturbed turbulence parameters. The anti-correlation appears only in the tropical ascent regime. In the Northern Hemisphere midlatitude and high latitude regimes, there is no significant correlation, and in the tropical marine low cloud and Southern Ocean regimes, the correlation is positive. These opposite correlations are primarily driven by opposing CF responses to perturbed parameters. We find that the mean-state turbulent mixing strength affects both CF and ERFaci, enabling strong correlations in certain regimes. This study highlights the complex linkages between CF and ERFaci through turbulent processes across diverse cloud regimes.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004756","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resolving Low Cloud Feedbacks Globally With E3SM High-Res MMF: Agreement With LES but Stronger Shortwave Effects","authors":"Liran Peng,&nbsp;Peter N. Blossey,&nbsp;Walter M. Hannah,&nbsp;Christopher S. Bretherton,&nbsp;Christopher R. Terai,&nbsp;Andrea M. Jenney,&nbsp;Savannah L. Ferretti,&nbsp;Hossein Parishani,&nbsp;Michael S. Pritchard","doi":"10.1029/2025MS005003","DOIUrl":"https://doi.org/10.1029/2025MS005003","url":null,"abstract":"<p>This study investigates low cloud feedback in a warmer climate using global simulations from the High-Resolution Multi-scale Modeling Framework (HR-MMF), which explicitly simulates small-scale eddies globally. Two 5-year simulations—one with present-day sea surface temperatures (SSTs) and a second with SSTs warmed uniformly by 4 K—reveal a positive global shortwave cloud radiative effect (SWCRE = 0.3 W/<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mi>m</mi>\u0000 <mn>2</mn>\u0000 </msup>\u0000 </mrow>\u0000 <annotation> ${mathrm{m}}^{2}$</annotation>\u0000 </semantics></math>/K), comparable to estimates from CMIP models. As the climate warms, significant reductions in low cloud cover occur over stratocumulus regions. This study is the first attempt to compare HR-MMF results with predictions from idealized large-eddy simulations from the CGILS intercomparison. Despite different underlying assumptions, we find qualitative agreement in SWCRE and inversion height changes between HR-MMF and CGILS predictions. This suggests reasonable credibility for the CGILS framework in predicting cloud responses under the out-of-sample conditions found in HR-MMF. However, the HR-MMF exhibits stronger SWCRE changes than predicted by CGILS. We explore potential causes for this discrepancy, examining variations in cloud-controlling factors (CCFs) and cloud conditions. Our results show a fairly homogeneous SWCRE response, with little systematic variation tied to the variations in CCFs. This reveals a dominant role for SST forcing in modulating SWCRE.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS005003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"E3SM-Arctic: Regionally Refined Coupled Model for Advanced Understanding of Arctic Systems Interactions","authors":"Yiling Huo,&nbsp;Hailong Wang,&nbsp;Milena Veneziani,&nbsp;Darin Comeau,&nbsp;Robert Osinski,&nbsp;Benjamin R. Hillman,&nbsp;Erika Roesler,&nbsp;Wieslaw Maslowski,&nbsp;Philip J. Rasch,&nbsp;Wilbert Weijer,&nbsp;Ian Baxter,&nbsp;Qiang Fu,&nbsp;Oluwayemi A. Garuba,&nbsp;Weiming Ma,&nbsp;Mark W. Seefeldt,&nbsp;Aodhan Sweeney,&nbsp;Mingxuan Wu,&nbsp;Jing Zhang,&nbsp;Xiangdong Zhang,&nbsp;Yu Zhang,&nbsp;Xylar Asay-Davis,&nbsp;Anthony P. Craig,&nbsp;Younjoo J. Lee,&nbsp;Wuyin Lin,&nbsp;Andrew F. Roberts,&nbsp;Jonathan D. Wolfe,&nbsp;Shixuan Zhang","doi":"10.1029/2024MS004726","DOIUrl":"https://doi.org/10.1029/2024MS004726","url":null,"abstract":"<p>Earth system models are essential tools for climate projections, but coarse resolutions limit regional accuracy, especially in the Arctic. Regionally refined meshes (RRMs) enhance resolution in key areas while maintaining computational efficiency. This paper provides an overview of the United States (U.S.) Department of Energy's (DOE's) Energy Exascale Earth System Model version 2.1 with an Arctic RRM, hereafter referred to as E3SMv2.1-Arctic, for the atmosphere (25 km), land (25 km), and ocean/ice (10 km) components. We evaluate the atmospheric component and its interactions with land, ocean, and cryosphere by comparing the RRM (E3SM2.1-Arctic) historical simulations (1950–2014) with the uniform low-resolution (LR) counterpart, reanalysis products, and observational data sets. The RRM generally reduces biases in the LR model, improving simulations of Arctic large-scale mean fields, such as precipitation, atmospheric circulation, clouds, atmospheric river frequency, and sea ice thickness. However, it introduces a seasonally dependent surface air temperature bias, reducing the LR cold bias in summer but enhancing the LR warm bias in winter, which contributes to the underestimated winter sea ice area and volume. Radiative feedback analysis shows similar climate feedback strengths in both model configurations, with the RRM exhibiting a more positive surface albedo feedback and contributing to a stronger surface warming than LR. These findings underscore the importance of high-resolution modeling for advancing our understanding of Arctic climate changes and their broader global impacts, although some persistent biases appear to be independent of model resolution at 10–100 km scales.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004726","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inferring Parameters in a Complex Land Surface Model by Combining Data Assimilation and Machine Learning","authors":"L. T. Keetz,&nbsp;K. Aalstad,&nbsp;R. A. Fisher,&nbsp;C. Poppe Terán,&nbsp;B. Naz,&nbsp;N. Pirk,&nbsp;Y. A. Yilmaz,&nbsp;O. Skarpaas","doi":"10.1029/2024MS004542","DOIUrl":"https://doi.org/10.1029/2024MS004542","url":null,"abstract":"<p>Complex Land Surface Models (LSMs) rely on a plethora of parameters. These parameters and the associated process formulations are often poorly constrained, which hampers reliable predictions of ecosystem dynamics and climate feedbacks. Robust and uncertainty-aware parameter estimation with observations is complicated by, for example, the high dimensionality of the model parameter space and the computational cost of LSM simulations. Herein, we adapt a novel Bayesian data assimilation (DA) and machine learning framework termed “calibrate, emulate, sample” (CES) to infer parameters in a widely-used LSM coupled with a demographic vegetation model (CLM-FATES). First, an iterative ensemble Kalman smoother provides an initial estimate of the posterior distribution (“calibrate”). Subsequently, a machine-learning-based emulator is trained on the resulting model-observation mismatches to predict outcomes for unseen parameter combinations (“emulate”). Finally, this emulator replaces CLM-FATES simulations in an adaptive Markov Chain Monte Carlo approach enabling computationally feasible posterior sampling with enhanced uncertainty quantification (“sample”). We test our implementation with synthetic and real observations representing a boreal forest site in southern Finland. We estimate a total of six plant-functional-type-specific photosynthetic parameters by assimilating evapotranspiration (ET) and gross primary production (GPP) flux data. CES provided the best estimates of the synthetic truth parameters when compared to data-blind emulator sampling designs while all approaches reduced model-observation errors compared to a default parameter simulation (GPP: <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>10</mn>\u0000 </mrow>\u0000 <annotation> ${-}10$</annotation>\u0000 </semantics></math>% to <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>30</mn>\u0000 </mrow>\u0000 <annotation> ${-}30$</annotation>\u0000 </semantics></math>%, ET: <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>4</mn>\u0000 </mrow>\u0000 <annotation> ${-}4$</annotation>\u0000 </semantics></math>% to <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>6</mn>\u0000 </mrow>\u0000 <annotation> ${-}6$</annotation>\u0000 </semantics></math>%). Although errors were also consistently reduced with real data, comparing the emulator designs was less conclusive, which we mainly attribute to equifinality, structural uncertainty within CLM-FATES, and/or unknown errors in the data that are not accounted for.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004542","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144332015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"E3SM-GCAM: A Synchronously Coupled Human Component in the E3SM Earth System Model Enables Novel Human-Earth Feedback Research","authors":"Alan V. Di Vittorio,&nbsp;Eva Sinha,&nbsp;Dalei Hao,&nbsp;Balwinder Singh,&nbsp;Katherine V. Calvin,&nbsp;Tim Shippert,&nbsp;Pralit Patel,&nbsp;Ben Bond-Lamberty","doi":"10.1029/2024MS004806","DOIUrl":"https://doi.org/10.1029/2024MS004806","url":null,"abstract":"<p>Modeling human-environment feedbacks is critical for assessing the effectiveness of climate change mitigation and adaptation strategies under a changing climate. The Energy Exascale Earth System Model (E3SM) now includes a human component, with the Global Change Analysis Model (GCAM) at its core, that is synchronously coupled with the land and atmosphere components through the E3SM coupling software. Terrestrial productivity is passed from E3SM to GCAM to make climate-responsive land use and CO₂ emission projections for the next 5-year period, which are interpolated and passed to E3SM annually. Key variables affected by the incorporation of these feedbacks include land use/cover change, crop prices, terrestrial carbon, local surface temperature, and climate extremes. Regional differences are more pronounced than global differences because the effects are driven primarily by differences in land use. This novel system enables a new type of scenario development and provides a powerful modeling framework that facilitates the addition of other feedbacks between these models. This system has the potential to explore how human responses to climate change impacts in a variety of sectors, including heating/cooling energy demand, water management, and energy production, may alter emissions trajectories and Earth system changes.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004806","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Key Gaps in Models' Physical Representation of Climate Intervention and Its Impacts","authors":"Sebastian D. Eastham,&nbsp;Amy H. Butler,&nbsp;Sarah J. Doherty,&nbsp;Blaž Gasparini,&nbsp;Simone Tilmes,&nbsp;Ewa M. Bednarz,&nbsp;Ulrike Burkhardt,&nbsp;Gabriel Chiodo,&nbsp;Daniel J. Cziczo,&nbsp;Michael S. Diamond,&nbsp;David W. Keith,&nbsp;Thomas Leisner,&nbsp;Douglas G. MacMartin,&nbsp;Johannes Quaas,&nbsp;Philip J. Rasch,&nbsp;Odran Sourdeval,&nbsp;Isabelle Steinke,&nbsp;Chelsea Thompson,&nbsp;Daniele Visioni,&nbsp;Robert Wood,&nbsp;Lili Xia,&nbsp;Pengfei Yu","doi":"10.1029/2024MS004872","DOIUrl":"https://doi.org/10.1029/2024MS004872","url":null,"abstract":"<p>Solar radiation modification (SRM) is increasingly discussed as a potential method to ameliorate some negative effects of climate change. However, unquantified uncertainties in physical and environmental impacts of SRM impede informed debate and decision making. Some uncertainties are due to lack of understanding of processes determining atmospheric effects of SRM and/or a lag in development of their representation in models, meaning even high-quality model intercomparisons will not necessarily reveal or address them. Although climate models at multiple scales are advancing in complexity, there are specific areas of uncertainty where additional model development (often requiring new observations) could significantly advance understanding of SRM's effects, and improve our ability to assess and weigh potential risks against those of choosing to not use SRM. We convene expert panels in the areas of atmospheric science most critical to understanding the three most widely discussed forms of SRM. Each identifies three key modeling gaps relevant to either stratospheric aerosols, cirrus, or low-altitude marine clouds. Within each area, key challenges remain in capturing impacts due to complex interactions in aerosol physics, atmospheric chemistry/dynamics, and aerosol-cloud interactions. Across all three, in addition to arguing for more observations, the panels argue that model development work to either leverage different capabilities of existing models, bridge scales across which relevant processes operate, or address known modeling gaps could advance understanding. By focusing on these knowledge gaps we believe the modeling community could advance understanding of SRM's physical risks and potential benefits, allowing better-informed decision-making about whether and how to use SRM.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004872","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Moisture Mode Oscillations in Steady-State Weak Temperature Gradient Simulations","authors":"Miguel Bernardez,&nbsp;Larissa Back","doi":"10.1029/2024MS004391","DOIUrl":"https://doi.org/10.1029/2024MS004391","url":null,"abstract":"<p>Weak Temperature Gradient modeling using a small cloud-resolving model admits multiple equilibria depending upon the initial model conditions. There were thought to be two equilibrium states, a moist precipitating state and a dry non-precipitating state. In this paper, we describe a periodic equilibrium which has oscillatory behavior from static boundary conditions. We show that the periodic oscillation has the characteristics of a moisture mode in the vertical dimension, instead of in the horizontal dimension. Further, we show that the oscillation occurs due to a balance between vertical advection and radiation, which can be described using a simple two vertical mode model. The first mode is related to the column relative humidity anomaly and a first baroclinic mode, while the second mode is related to a moisture dipole centered around 600 hPa and a second baroclinic mode. The first mode is associated with the generation of a moisture dipole, while the second mode is associated with the generation of a column moisture anomaly.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004391","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Cloud Feedbacks Over the Atlantic With Bias-Corrected Downscaling","authors":"Shuchang Liu,&nbsp;Christian Zeman,&nbsp;Christoph Schär","doi":"10.1029/2024MS004661","DOIUrl":"https://doi.org/10.1029/2024MS004661","url":null,"abstract":"<p>Clouds exert a significant impact on global temperatures and climate change. Cloud-radiative feedback (CRF) is one of the major sources of climate change uncertainty. Understanding CRF is therefore crucial for accurate climate projections. Biases like the double-ITCZ problem in Global Climate Models (GCMs) hamper precise climate projections. Here, we explore a bias-corrected downscaling method to constrain the cloud feedback uncertainties in the tropical and sub-tropical Atlantic region. We use regional climate model (RCM) simulations with convection permitting resolution, driven by debiased driving fields from three different global climate models (GCMs). Bias-corrected downscaling significantly reduces biases in ITCZ intensity and position, eliminating the double-ITCZ bias across all six experiments (three GCMs for historical and future periods). We explore the new methodology's potential to investigate the CRF in comparison to that of the driving GCMs. Results indicate that additional GCMs and RCMs are necessary for a more comprehensive uncertainty estimation and more conclusive results, while our simulations suggest a potentially narrower range of CRF over the tropical and subtropical Atlantic, primarily due to an improved representation of stratocumulus clouds. Our study highlights the potential of bias-corrected downscaling in constraining the uncertainty of simulations and estimates of cloud feedback and equilibrium climate sensitivity. The results advocate for further simulations with additional RCMs and domains for a more comprehensive analysis.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004661","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144292448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing CO2 Fluxes for European Peatlands in ORCHIDEE-PEAT With Multiple Plant Functional Types","authors":"Liyang Liu,&nbsp;Chunjing Qiu,&nbsp;Yi Xi,&nbsp;Elodie Salmon,&nbsp;Aram Kalhori,&nbsp;Rebekka R. E. Artz,&nbsp;Christophe Guimbaud,&nbsp;Matthias Peichl,&nbsp;Joshua L. Ratcliffe,&nbsp;Koffi Dodji Noumonvi,&nbsp;Efrén López-Blanco,&nbsp;Jiří Dušek,&nbsp;Tiina Markkanen,&nbsp;Torsten Sachs,&nbsp;Mika Aurela,&nbsp;Thu-Hang Nguyen,&nbsp;Annalea Lohila,&nbsp;Ivan Mammarella,&nbsp;Philippe Ciais","doi":"10.1029/2025MS004940","DOIUrl":"https://doi.org/10.1029/2025MS004940","url":null,"abstract":"<p>Peatlands are significant carbon reservoirs vulnerable to climate change and land use change such as drainage for cultivation or forestry. We modified the ORCHIDEE-PEAT global land surface model, which has a detailed description of peat processes, by incorporating three new peatland-specific plant functional types (PFTs), namely deciduous broadleaf shrub, moss and lichen, as well as evergreen needleleaf tree in addition to previously peatland graminoid PFT to simulate peatland vegetation dynamic and soil CO₂ fluxes. Model parameters controlling photosynthesis, autotrophic respiration, and carbon decomposition have been optimized using eddy-covariance observations from 14 European peatlands and a Bayesian optimization approach. Optimization was conducted for each individual site (single-site calibration) or all sites simultaneously (multi-site calibration). Single-site calibration performed better, particularly for gross primary production (GPP), with root mean square deviation (RMSD) reduced by 53%. While multi-site calibration showed limited improvement (e.g., RMSD of GPP reduced by 22%) due to the model's inability to account for spatial parameter variations under different climatic contexts (trait-climate correlations). Site-optimized parameters, such as <b>Q</b>_<b>10</b>, the temperature sensitivity of heterotrophic respiration, revealed strong empirical relationships with environmental factors, such as air temperature. For instance, <b>Q</b>_<b>10</b> decreased significantly at warmer sites, consistent with independent field data. To improve the model by using the lessons from single-site optimization, we incorporated two key trait-climate relationships for <b>Q</b>_<b>10</b> and <b><i>V</i></b>_<b>cmax</b> (maximum carboxylation rate) into a new version of the ORCHIDEE-PEAT models. Using this description of spatial variability of parameters holds significant promise for improving the accuracy of carbon cycle simulations in peatlands.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS004940","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144299789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Constraining Black Carbon Aging in Global Models to Reflect Timescales for Internal Mixing","authors":"Laura Fierce,&nbsp;Yinrui Li,&nbsp;Yan Feng,&nbsp;Nicole Riemer,&nbsp;Nick A. J. Schutgens,&nbsp;Allison C. Aiken,&nbsp;Manvendra K. Dubey,&nbsp;Po-Lun Ma,&nbsp;Donald Wuebbles","doi":"10.1029/2024MS004471","DOIUrl":"https://doi.org/10.1029/2024MS004471","url":null,"abstract":"<p>The radiative effects of black carbon depend critically on its atmospheric lifetime, which is controlled by the rate at which freshly emitted combustion particles become internally mixed with other aerosol components. Global aerosol models strive to represent this process, but the timescale for aerosol mixing is not easily constrained using observations. In this study, we apply a timescale parameterization derived from particle-resolved simulations to quantify, in a global aerosol model, the timescale for internal mixing. We show that, while highly variable, the average timescale for internal mixing is approximately 3 hr, which is much shorter than the 24-hr aging timescale traditionally applied in bulk aerosol models. We then use the mixing timescale to constrain the aging criterion in the Modal Aerosol Module. Our analysis reveals that, to best reflect timescales for internal mixing, modal models should assume that particles transition from the hydrophobic (fresh) to the hydrophilic (aged) class once they accumulate a coating thickness equal to four monolayers of sulfuric acid, as opposed to the model's current aging criterion of eight monolayers. We show that, in remote regions like the Arctic and Antarctic, predictions of black carbon loading and its seasonal variation are particularly sensitive to the model representation of aging. By constraining aging in global models to reflect mixing timescales simulated by the particle-resolved model, we eliminate one of the free parameters governing black carbon's long-range transport and spatiotemporal distribution.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004471","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}