Journal of Advances in Modeling Earth Systems最新文献

Emissions-driven (prognostic CO₂) simulations are essential for representing two-way carbon-climate feedback in Earth System Models. We present an emissions-driven land–atmosphere coupled biogeochemistry (BGC) configuration (BGCLNDATM_progCO2) in version 2.1 of the Energy Exascale Earth System Model (E3SMv2.1). This is the first E3SM configuration that performs land-atmosphere emission-hindcasts. Here, we document its implementation, evaluate the model's performance against observations and other models, and propose a structured evaluation protocol for such emissions-driven simulations. We conducted transient historical simulations (1850–2014) with BGCLNDATM_progCO2 and compare them to reference simulations—a land-atmosphere coupled simulation without BGC and a standalone land simulation with BGC, both using prescribed CO₂ concentrations—and to observations. BGCLNDATM_progCO2 overestimates atmospheric CO₂ concentrations by 11–23 ppm yet stays within the 40-ppm spread CMIP6 emission-driven models and retains physical climate properties comparable to the reference runs. The CO₂ biases are partly attributed to underrepresented oceanic CO₂ uptake and inadequate representations of some terrestrial processes. In general, introducing prognostic CO₂ did not change physical climate metrics at the global scale but had larger regional effects, particularly over land where spatially heterogeneous CO₂ and prognostic leaf area index influenced surface energy balance. Finally, we propose a general evaluation protocol including spin-up assessment, atmospheric CO₂ benchmarking, physical climate evaluation, and land biogeochemical analysis to support scientific rigor and facilitate inter-model comparisons. The new configuration lays the groundwork for future enhancements, including improved terrestrial biogeochemical processes, integrated marine biogeochemistry, and additional human–Earth system interactions. These developments advance E3SM toward fully coupled emissions-driven simulations, enabling more accurate carbon–climate feedback projections and informing mitigation policy by providing physically consistent carbon-budget metrics for mitigation scenarios.

The mangrove ecosystem is characterized by high carbon sequestration rates and plays a crucial role for the exchange of carbon between land and ocean. Understanding the carbon dynamics of mangroves under climate change and human disturbances is therefore essential for quantifying their contributions to global carbon cycle. However, most land surface models do not have a specific module for mangroves, leading to potential biases in simulating the uptake of atmospheric carbon by terrestrial ecosystems and the carbon budget of tropical countries. In this study, we introduced a new plant functional type for mangroves into the land surface model ORCHIDEE-MICT-PEAT-LEAK. We added the mangrove-specific carbon pools of aboveground roots and a new carbon allocation scheme. We also added the effects of salinity and tidal inundation on mangrove productivity. We then calibrated and optimized the parameters related to photosynthesis, autotrophic respiration, carbon allocation and mortality using data collected from literature reviews. Compared to the original model version, the modified version shows greatly improved performance when evaluated against the observational data sets from field measurements. Specifically, ORCHIDEE-MAN can generally reproduce the spatial distribution of aboveground biomass density from satellite-based map, as well as the seasonal cycles of gross primary productivity observed at three eddy covariance flux towers for mangroves. This model version with the mangrove module provides a useful tool for understanding the carbon cycle processes and estimating carbon budgets in mangrove ecosystems. The processes and parameters described here may support the development of mangrove module in other land surface models.

For over 60 years, an approximation involving spherical geopotentials has underlain the representation of gravity in global numerical models of Earth's atmosphere and oceans. This article explores how departures from sphericity can be allowed by assuming spheroidal geopotentials instead. A route to more accurate model formulation is indicated, and the theoretical basis of the classical spherical-geopotential approximation is illuminated too. An overview—from the time of Newton to the present—is given of the development of zeroth- and first-order approximations to the geopotential field in terms of two small non-dimensional parameters. The importance of using geopotential coordinate systems in atmospheric and oceanic models is emphasized. Early suggestions for such systems using non-spherical coordinates involved qualitatively inappropriate choices of ellipsoids derived from families of confocal ellipses. Specific examples of appropriate ellipsoid choices are considered before presentation of the recently developed Geophysically Realistic, Ellipsoidal, Analytically Tractable (GREAT) system. This is based on a suitably constructed geopotential field approximation, first order accurate, from which—without further approximation—may be analytically derived equations for geopotential surfaces and surfaces orthogonal to them. GREAT coordinates satisfy stated desiderata for geopotential coordinate systems and are applicable both above and below Earth's geoid (assumed to coincide with the WGS 84 [2004, https://gis-lab.info/docs/nima-tr8350.2-wgs84fin.pdf] reference ellipsoid to an excellent approximation). GREAT-coordinate analysis provides justification for the classical spherical-geopotential approximation: It is revealed as a mathematical limit, but not a physically realizable one. Attention is drawn to a certain partially spherical limit that is realizable physically.

Urgent applications like wildfire management and renewable energy generation require precise, localized weather forecasts near the Earth's surface. However, forecasts produced by machine learning models or numerical weather prediction systems are typically generated on large-scale regular grids, where direct downscaling fails to capture fine-grained, near-surface weather patterns. In this work, we propose a multi-modal transformer model trained end-to-end to downscale gridded forecasts to off-grid locations of interest. Our models directly combine local historical weather observations (e.g., wind, temperature, dewpoint) with gridded forecasts to produce locally accurate predictions at various lead times. Multiple data modalities are collected and concatenated at station-level locations, treated as a token at each station. Using self-attention, the token corresponding to the target location aggregates information from its neighboring tokens. Experiments using weather stations across the Northeastern United States show that our model outperforms a range of data-driven and non-data-driven off-grid forecasting methods. They also reveal that direct input of station data provides a marked improvement in local weather forecasting accuracy, reducing the prediction error by up to 80% compared to pure gridded data based models. This approach demonstrates how to bridge the gap between large-scale weather models and locally accurate forecasts to support high-stakes, location-sensitive decision-making.

A hybrid ensemble data assimilation (DA) system is implemented for a coupled physical–biogeochemical ecosystem model of the Red Sea using MITgcm and NBLING at 4 km resolution, marking the first application of its kind in the region. The methodology combines a temporally varying ensemble from the Ensemble Adjustment Kalman Filter with a quasi-static monthly ensemble, implemented through the DA Research Testbed. Physical (satellite sea surface temperature, altimetry, in situ temperature and salinity) and biogeochemical (satellite chlorophyll) observations are assimilated, accounting for uncertainties in atmospheric forcing. Two configurations are evaluated: weakly coupled DA (weakly coupled data assimilation [WCDA]), which updates physical and biogeochemical states independently, and strongly coupled DA (strongly coupled data assimilation [SCDA]), which updates both using all observations. Sensitivity experiments assess the influence of assimilated observations on biogeochemical states, validated against independent temperature, salinity, sea surface height, chlorophyll, and oxygen data. Results demonstrate the benefits of joint assimilation in the Red Sea but also highlight challenges with SCDA. While SCDA improves the biogeochemical state relative to the free run, WCDA yields more robust physical estimates and better chlorophyll forecasts, particularly in subsurface layers. Physical assimilation through WCDA enhances biogeochemical fields throughout the water column, often exceeding 0.2 mg m⁻³ and the ensemble spread. Surface chlorophyll assimilation further improves WCDA surface predictions, though subsurface impacts are mixed. These findings emphasize both the value of WCDA and the need for further development to fully realize SCDA's potential for coupled physical–biogeochemical DA.

This paper introduces an Earth system modeling testbed for predicting aerosols and aerosol-cloud interactions (ACIs) at convection-permitting scales. Using the Energy Exascale Earth System Model (E3SM) version 2 with a four-mode Modal Aerosol Module, we conduct simulations at 3.25 km resolution on a regionally refined mesh (RRM) across four regions with distinct aerosol and cloud regimes. Results are compared with the standard 100 km E3SM configuration and evaluated against satellite, aircraft, and ground-based observations. We find that increasing model resolution improves heavy precipitation simulation but amplifies positive bias in light drizzle at coarse resolution. These resolution-induced changes affect cloud and aerosol properties to varying degrees across regions. Generally, cloud cover and liquid water path (LWP) show better agreement with satellite retrievals at 3.25 km, though surface-based comparisons suggest otherwise. Aerosol composition remains poorly represented at both resolutions. The RRM increases Aitken mode aerosol number concentrations via enhanced new particle formation. However, accumulation mode aerosols are decreased at higher resolution as aerosol removals become more efficient. This partially contributes to fewer cloud condensation nuclei (CCN) and lower cloud droplet number concentrations ($��N_d�$), which produces larger model biases in some scenarios. These findings suggest that solely increasing horizontal resolution to kilometer scales is insufficient to broadly improve aerosol and cloud predictions without concurrent advancements in physical and chemical process representations. Nonetheless, the RRM moderately improves key ACI relationships such as CCN-$��N_d�$ correlation, reflecting enhanced aerosol activation representation. The LWP-$��N_d�$ relationship is also better captured by RRM, suggesting a better characterization of LWP adjustment.

Ecosystem respiration (ER) is the second-largest terrestrial carbon flux, yet ecosystem models often fail to capture its variability under climatic extremes. The increasing frequency and severity of precipitation and drought extremes pose substantial challenges to accurately predicting ER. It remains unclear whether parameters calibrated under normal climates can reliably predict ER under extreme events. Here, we used long-term eddy covariance data from a semi-arid grassland to investigate the predictability of conventional linear and non-linear microbial models. Both models were parameterized using a Monte Carlo Markov Chain assimilation approach based on data from normal climatic years. However, both exhibited poor performance in simulating daily ER during extreme drought and wet years, due to significant parameter divergence between normal and extreme years. We derived model parameters for extreme drought and wet years, revealing pronounced divergence: all parameters in the linear and microbial model varied significantly between normal and extreme years, with ∼29% displaying high variability (coefficient of variation >0.3). Furthermore, principal component analysis revealed substantial parameter divergence among hydrological regimes. Sensitivity analysis showed 93% of parameters exhibit asymmetric responses in extreme drought and wet years. These results indicate that fixed parameters calibrated under normal climatic conditions cannot represent the emergent properties of ecosystems during extreme events. Our findings highlight that varying parameters are not merely a technical adjustment but a fundamental requirement for improving the predictability of ER under climate extremes.

The Madden-Julian Oscillation (MJO) and the El Niño-Southern Oscillation (ENSO) are two dominant modes of tropical climate variability, each with profound global weather impacts. While their individual dynamics have been widely studied, their coupled interactions, particularly in the context of ENSO complexity, including spatial diversity (Central Pacific [CP] vs. Eastern Pacific events), temporal evolution (single-year and multi-year events), and intensity variations (moderate to extreme events), have received limited attention in modeling studies. In this paper, a simple intermediate coupled MJO-ENSO model is developed to address critical gaps in understanding their bidirectional feedback and its role in modulating ENSO complexity. The model integrates multiscale processes, bridging intraseasonal (MJO), interannual (ENSO), and decadal (Walker circulation) variability. Key mechanisms include: (a) interannual sea surface temperature (SST) modulating MJO through latent heat and background states, (b) MJO-induced wind forcing triggering diverse ENSO events, and (c) decadal variability modulating the strength and occurrence frequency of Eastern Pacific and CP events. Effective stochastic parameterizations are incorporated to improve the characterization of multiscale MJO-ENSO interactions and the emergence of intermittency and extremes. The model captures several crucial observed MJO and ENSO features, including non-Gaussian statistics, seasonal cycles, energy spectra, and spatial event patterns. It also reproduces critical MJO-ENSO interactions: warm pool edge extension, convective activity adjustments that modulate SST, and ENSO's dependence on MJO-driven easterly and westerly wind anomalies. The model provides a useful tool to analyze long-term variations. It also advances the understanding of ENSO extreme events and their remote impacts, as well as seasonal forecasting and climate resilience.

In 2023, atmospheric methane (CH₄) saw a decrease in the annual growth rate following record increases from 2020 to 2022. Recent changes in CH₄ remain difficult to quantify due to delays in near-real-time (NRT) carbon cycle estimates. However, NRT models and subseasonal-to-seasonal (S2S) forecasting can provide the opportunity to analyze ongoing climate events, leading to a deeper understanding of the methane cycle. We applied the Lund-Potsdam-Jena Earth-Observation-SIMulator (LPJ-EOSIM) to investigate methane emissions in 2023, focusing on a short-term case study using S2S forecasts to capture wetland methane dynamics during the 2023 El Niño-related drought in Amazonia. We modeled a 2.64 ± 5.73 Tg CH₄ decrease in global wetland methane emissions from 2022 to 2023 using an ensemble of four climate driver data sets, corresponding to ∼0.95 ppb (2.77 ppb/Tg CH₄), accounting for 29% of the −3.33 ppb change in atmospheric methane annual growth rate. In 2023, LPJ-EOSIM showed tropical wetland (90°S–30°N) emissions decreased by 4.47 ± 4.79 Tg CH₄, with Amazonia accounting for 68% of this reduction (3.05 ± 2.40 Tg CH₄). Meanwhile, emissions across northern latitudes (>30°N) increased by 1.83 ± 1.21 Tg CH₄, partially offsetting the large tropical decrease. We show that the NRT LPJ-EOSIM coupled with S2S forecasts could help forecast these anomalies in Amazonia, with reliable regional lead times at 3–5 months when forecasts were initialized following the wet-to-dry season transition. NRT products and S2S forecasts can provide early warning systems for improved strategic wetland CH₄ monitoring to assess impacts of climate anomalies on the carbon cycle and reduce the gap in the global methane budget.