NOAA Geophysical Fluid Dynamics Laboratory

Abstract Understanding the weather and climate of Mesoamerica and the Caribbean remains challenging due to complex hydroclimate interactions, limited observations, and poor representation of regional processes in global models. We introduce the Mesoamerica Affinity Group (MAAG), a National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) and community initiative that fosters research collaboration to advance weather and climate science, develop convection-permitting datasets, and promote knowledge exchange. MAAG’s first major contribution is a 2-week convection-permitting simulation of Hurricane Maria (2017) using Model for Prediction Across Scales–Atmosphere (MPAS-A), featuring a novel regional 15- to 3-km variable-resolution mesh over the region. Initial evaluation shows that MPAS-A captures key features like precipitation patterns, the intertropical convergence zone, and low-level jets. Some biases remain, particularly in enhanced land convection and slight deviations in Maria’s track. This novel dataset, now publicly available through NCAR’s Data Archive, supports studies of other extreme events and mesoscale convective systems active during the same period. It offers a valuable resource for the research community. MAAG is a new but rapidly growing initiative achieving notable milestones in a short time. It serves as a collaborative platform for codesigning high-resolution modeling experiments aimed at producing actionable weather and climate information. We invite the community to join MAAG, explore this initial dataset, and advance regional weather and climate research. Significance Statement The Mesoamerica Affinity Group (MAAG), a National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) and community initiative, aims at addressing the complex challenges of understanding weather and climate in Mesoamerica and the Caribbean. MAAG’s first major achievement is a 2-week convection-permitting simulation of Hurricane Maria (2017) using a novel 15- to 3-km variable-resolution mesh. This dataset accurately captures key regional features and is publicly available through NCAR’s Data Archive. By fostering collaboration through data production and sharing, monthly group meetings that serve as a platform for networking and knowledge exchange, and the development of advanced high-resolution datasets, MAAG provides a vital resource for advancing regional weather and climate science. The initiative is rapidly growing, serving as a platform for codesigned modeling experiments aimed at producing actionable climate information for academia and different sectors. We invite the scientific community to join MAAG and advance research in this critical region.

<strong class="journal-contentHeaderColor">Abstract.</strong> Surface melt strongly influences the thermodynamics and stability of Antarctic ice shelves, yet firn models remain poorly constrained due to scarce observations. We present a framework that integrates satellite-borne microwave radiometry with firn modelling to evaluate meltwater generation and infiltration. Using the Glacier Energy and Mass Balance (GEMB) surface energy balance model, we simulate snow and firn temperature, density, and liquid water content (LWC) on the Shackleton Ice Shelf. These outputs drive the Microwave Emission of Model of Layered Snowpacks (MEMLS) radiative transfer model to simulate microwave brightness temperatures (TB), which we compare with satellite radiometer observations at 1.4 and 36.5 GHz. Because TB is highly sensitive to LWC, this approach provides a powerful constraint on firn thermodynamics in data-sparse regions. Our results reveal that realistic simulation of meltwater percolation and refreezing requires substantially finer vertical resolution than typically employed. Incorporating radiometric constraints thus improves firn evolution modelling and advances projections of Antarctic ice shelf stability.

<strong class="journal-contentHeaderColor">Abstract.</strong> Aerosols in the upper troposphere play an important role in Earth&rsquo;s radiative balance and atmospheric composition. Satellite observations have revealed a recurrent enhancement of aerosol extinction coefficient (AEC) in the upper troposphere and near the tropopause over the Asian summer monsoon (ASM) anticyclone (ASMA) region during July&ndash;August. However, substantial uncertainties remain regarding (i) the influence of ASM dynamics and climate variability on these aerosols, (ii) the extent to which the upper-tropospheric aerosol trends reflect changes in surface pollutant emissions, and (iii) the ability of global models to simulate aerosol amounts, variability, and key controlling processes in the upper-tropospheric ASMA region. Here, we present results from an AeroCom-coordinated global multi-model study addressing these issues. Using simulations from nine models for 2000&ndash;2018, we find large inter-model differences in non-volcanic AEC over the upper-tropospheric ASMA region, with coefficients of variation ranging from 64 % to 86 %. Approximately half of this spread is attributable to differences in transport and wet removal processes, as diagnosed using common tracers, with discrepancies in wet removal contributing about eight times more than those associated with transport. The multi-model ensemble indicates an overall increase in non-volcanic AEC over the past two decades, consistent with rising anthropogenic emissions in Asia, while interannual variability is linked to climate variability as represented by the Multivariate ENSO Index. Through comparison with satellite observations, we further identify persistent model deficiencies, particularly in the representation of volcanic aerosols, and highlight priorities for future coordinated model developments and evaluation.

Abstract The continuous increase in atmospheric methane (CH 4 ) concentrations over the past few decades has become a major concern due to its strong role as a greenhouse gas contributing to climate change. In this work, we investigate the changes in the global methane budget using a global chemistry‐climate model constrained with methane and its isotopic observations. We apply spatially‐resolved isotopic signatures to better constrain the methane sources and include methane‐hydroxyl radical (OH) feedback to better represent methane sinks and lifetime in the model. While anthropogenic activities are found to be mainly responsible for the methane increase since the 1980s, the increasing OH trend simulated by the model plays a critical role in the global methane evolution. We find the observed post‐2006 shift of δ 13 CH 4 can be explained by increases in 13 C‐depleted agricultural and waste emissions in the tropics, coupled with decreasing 13 C‐enriched biomass burning emissions and an increasing OH trend. We also find post‐2006 emission increases in energy and agriculture sectors are large enough to offset the increasing sinks (due to increasing OH), and therefore are shown to contribute to the post‐2006 renewed methane growth. With CH 4 ‐OH feedback included in the model, the results show an increasing sensitivity to emission increases on methane concentrations and lifetime. Our study underscores the importance of OH in the global methane evolution. Neglecting changes in OH could potentially lead to misinterpreting emission changes with respect to the long‐term observations of methane and δ 13 CH 4 .

Abstract National Centers for Environmental Prediction turbulent kinetic energy (TKE)‐based eddy‐diffusivity mass‐flux (EDMF) scheme is implemented in Geophysical Fluid Dynamics Laboratory atmospheric model (AM4.0) for improving the physical consistency of subgrid‐scale planetary boundary layer (PBL) turbulence parameterization. The mass flux (MF) component represents vertically coherent convective structures responsible for countergradient transport in the upper PBL, which the original AM4.0's ED‐only scheme cannot represent. Consequently, AM4.0 with EDMF produces a deeper and more well‐mixed PBL, leading to better zonal‐mean vertical temperature and humidity profiles and reduced near‐surface wet bias over subtropical and midlatitude oceans. Other model performance changes are generally minor, such as similar biases in global top of atmosphere (TOA) net radiation and shortwave cloud radiative effects, small and compensating changes in low cloud amount and cloud liquid water path, improved low‐level equatorial easterlies but deteriorated extratropical westerlies, slightly increased global‐mean precipitation, and weaker TOA radiative response to uniform sea surface warming. Three adaptations of EDMF are important for its performance at AM4.0's relatively coarse vertical resolution: limiting the overshoot of MF updraft above PBL‐top, reducing the ED‐induced mixing across PBL‐top, and disabling the MF transport of TKE. Low clouds and their radiative effects are also sensitive to four EDMF parameters that control the ED in the lower and upper PBL respectively, the TKE dissipation rate, and the lateral entrainment of MF updraft and downdraft. An automatic linear tuning of these parameters slightly improves the radiative bias, especially for the coastal stratocumulus. More substantial improvements likely require formulation updates of the EDMF scheme and its coupling with other AM4.0 model components.

We showcase a hybrid modeling framework that embeds machine learning (ML) inference into the Geophysical Fluid Dynamics Laboratory Seamless System for Prediction and Earth System Research (SPEAR) climate model for online sea ice bias correction during a set of global fully coupled 1-year retrospective forecasts. We compare two hybrid versions of SPEAR to understand the importance of exposing ML models to coupled ice-atmosphere-ocean feedbacks before implementation into fully coupled simulations: HybridCPL (couple trained; with feedbacks) and HybridIO (ice ocean trained; without feedbacks). Relative to SPEAR, HybridCPL systematically reduces seasonal forecast errors in the Arctic and considerably reduces Antarctic errors for target months May to December, with >2× error reduction in 4- to 6-month lead forecasts of Antarctic winter sea ice extent. Meanwhile, HybridIO suffers from out-of-sample behavior that can trigger a chain of Southern Ocean feedbacks, leading to ice-free Antarctic summers. Our results emphasize that ML can demonstrably improve numerical sea ice prediction capabilities and that exposing ML models to coupled ice-atmosphere-ocean processes is essential for generalization in fully coupled simulations.

Abstract In this study, we propose a method to predict seasonal Atlantic hurricane activity by utilizing generated maps that depict the spatial distribution of two key predictors: sea surface temperature (SST) and outgoing longwave radiation (OLR). Our approach employs a convolutional neural network (CNN) system that processes SST and OLR maps as input images to predict the accumulated cyclone energy (ACE) index, which is classified into three activity levels: high, medium, and low. Using these two different sets of input data, we developed two separate models based on our CNN architecture. The first model provides an early season prediction, 4 months in advance, using SST data from January to identify high-activity seasons. The second model delivers high forecast skill for three-class prediction closer to the season start, using July OLR data. Both models offer higher accuracy compared to past seasonal predictions issued in May and August.

<strong class="journal-contentHeaderColor">Abstract.</strong> From mid-2003 to mid-2024, a decrease in low-cloud amount enhanced the absorption of solar radiation by 0.22&plusmn;0.07 W m^-2 decade^-1 (&plusmn;1<em>&sigma;</em> range), accelerating the energy imbalance trend during that period (0.44 W m^-2 decade^-1). Through controlling factor analysis, here we show that the low-cloud trend is due to a combination of cloud feedback and adjustments to aerosols and greenhouse gases (respectively 0.07&plusmn;0.01, 0.06&plusmn;0.01, and 0.05&plusmn;0.03 W m^-2 decade^-1), which jointly account for 82 % of the trend. The contribution of natural climate variability is weak but uncertain (0.03&plusmn;0.07 W m^-2 decade^-1), owing to a poorly constrained trend in boundary-layer inversion strength. Importantly, the observed low-cloud radiative trend lies well within the range of values simulated by contemporary global climate models under conditions close to present day. Any systematic model error in the representation of present-day global energy imbalance trends is thus likely to originate in processes other than low clouds.

<strong class="journal-contentHeaderColor">Abstract.</strong> A comparison of simulated and observed overturning transports and related properties across the Overturning in the Subpolar North Atlantic Program (OSNAP) sections for the 2014&ndash;2022 period is presented, considering both depth and density space transports. The effort was motivated by the observational transport estimates at both OSNAP-West (OW) and OSNAP-East (OE) sections which show a minor role for the Labrador Sea (LS) in setting the mean and variability of the overturning in the subpolar North Atlantic. There are 9 participating groups from around the world, contributing a total of 18 ocean &ndash; sea simulations with 6 different ocean models. The simulations use a common set of interannually-varying atmospheric forcing datasets. The horizontal resolutions of the simulations range from nominal 1&deg; to eddy-resolving resolutions of 0.1&deg;&ndash;0.05&deg;. While there are many differences between the simulations and observations as well as among the individual simulations in terms of transport properties, the simulations show significantly larger transports at OE than at OW in general agreement with the observations. Analyzing overturning circulations in both depth and density space together provides a more complete picture of the overturning properties and features. This analysis also reveals that, in both the simulations and observations, northward and southward flows substantially cancel each other, producing much smaller residual (total) transports. Such cancellations tend to be much more prominent in depth space than in density space. In general, the observed transport features are captured better at OE than OW. The simulations generally show larger (smaller) transports with positive (negative) temperature and salinity biases in the upper ocean near the OSNAP sections, but with no such relationship with density biases. In high-resolution simulations, the transport profiles agree better with the observations in general, but challenges remain in some other metrics considered in our analysis. When transports are calculated using a density referenced to 2000-m depth, rather than the ocean surface, the relative contributions of transports at OW increase modestly.

The impact of increasing anthropogenic hydrogen (H2) emissions on Earth’s radiative balance depends on the soil microbial H2 sink—the largest and most uncertain term in the global H2 budget. Soil moisture is a primary but poorly quantified control regulating the soil sink. Here, we assess the sensitivity of microbial H2 oxidation to soil moisture in laboratory experiments with temperate and arid soils spanning distinct textures. We report H2 oxidizer activity down to –70 to –100 MPa water potentials across soils, which are among the driest conditions reported for microbial activity and are much drier than assumed in global simulations of H2. Using genome-resolved meta-omics, we link H2 oxidation dynamics in temperate soils to specific desiccation-adapted microbial taxa that contribute differentially to H2 uptake along the moisture gradient. Through global simulations, we show that our observationally constrained drier moisture threshold increases the contribution of arid and semi-arid regions for soil H2 uptake by 4-7 percentage points (pp), while decreasing the contribution of temperate and continental regions (−7 pp). Our results highlight the importance of H2 uptake under extreme hydrological conditions, particularly the roles of desertification, dryland expansion, and H2-oxidizer ecophysiology in modulating long-term changes in H2 uptake.