Earth System Research Articles

Reduced Arctic Ocean CO2 uptake due to coastal permafrost erosion

Arctic coastal permafrost erosion is projected to increase by a factor of 2–3 by 2100. However, organic matter fluxes from the coastal permafrost into the ocean have not been considered in Earth system models so far. Here we represent coastal permafrost erosion in an Earth system model and perform simulations with varying permafrost organic matter properties, such as sinking fraction and nutrient content. We find that coastal erosion reduces the Arctic Ocean CO2 uptake from the atmosphere in all simulations: by 4.6–13.2 TgC yr−1 by 2100, which is ~7–14% of the Inner Arctic Ocean uptake. We show that coastal permafrost erosion exerts a positive biogeochemical feedback on climate, increasing atmospheric CO2 by 1–2 TgC yr−1 per °C of increase in global surface air temperature. Our work will allow coastal permafrost erosion to be considered in future climate change assessments.

Riverine dissolved organic matter transformations increase with watershed area, water residence time, and Damköhler numbers in nested watersheds

Quantifying the relative influence of factors and processes controlling riverine ecosystem function is essential to predicting future conditions under global change. Dissolved organic matter (DOM) is a fundamental component of riverine ecosystems that fuels microbial food webs, influences nutrient and light availability, and represents a significant carbon flux globally. The heterogeneous nature of DOM molecular composition and its propensity for interaction (i.e., functional diversity) can characterize riverine ecosystem function across spatiotemporal scales. To investigate fundamental drivers of DOM diversity, we collected seasonal water samples from 42 nested locations within five watersheds spanning multiple watershed sizes (~ 5 to 30,000 km2) across the United States. Patterns in DOM molecular richness, aromaticity, relative abundance of N-containing formulas, and putative biochemical transformations derived from high-resolution mass spectrometry were assessed across gradients of explanatory variables associated with watershed characteristics (e.g., watershed area, water residence time, land cover). We found that putative biochemical transformations were more strongly related to explanatory variables across watersheds than common bulk DOM parameters and that watershed area, surface water residence time and derived Damköhler numbers representing DOM reactivity timescales were strong predictors of DOM diversity. The data also indicate that catchment-specific land cover factors can significantly influence DOM diversity in diverging directions. Overall, the results highlight the importance of considering water residence time and land cover when interpreting longitudinal patterns in DOM chemistry and the continued challenge of identifying generalizable drivers that are transferable across watershed and regional scales for application in Earth system models. This work also introduces a Findable Accessible Interoperable Reusable (FAIR) dataset (> 300 samples) to the community for future syntheses.

Modelling Mediterranean ocean biogeochemistry of the Last Glacial Maximum

Abstract. We present results of simulations performed with a physical–biogeochemical ocean model of the Mediterranean Sea for the Last Glacial Maximum (LGM) and analyse the differences in physical and biochemical states between the historical period and the LGM. Long-term simulations with an Earth system model based on ice sheet reconstructions provide the necessary atmospheric forcing data, oceanic boundary conditions at the entrance to the Mediterranean Sea, and river discharge to the entire basin. Our regional model accounts for changes in bathymetry due to ice sheet volume changes, reduction in atmospheric CO2 concentration, and an adjusted aeolian dust and iron deposition. The physical ocean state of the Mediterranean during the LGM shows a reduced baroclinic water exchange at the Strait of Gibraltar, a more sluggish zonal overturning circulation, and the relocation of intermediate and deep-water-formation areas – all in line with estimates from palaeo-sediment records or previous modelling efforts. Most striking features of the biogeochemical realm are a reduction in the net primary production, an accumulation of nutrients below the euphotic zone, and an increase in the organic matter deposition at the seafloor. This seeming contradiction of increased organic matter deposition and decreased net primary production challenges our view of possible changes in surface biological processes during the LGM. We attribute the origin of a reduced net primary production to the interplay of increased stability of the upper water column, changed zonal water transport at intermediate depths, and lower water temperatures, which slow down all biological processes during the LGM. Cold water temperatures also affect the remineralisation rates of organic material, which explains the simulated increase in the organic matter deposition, which is in good agreement with sediment proxy records. In addition, we discuss changes in an artificial tracer which captures the surface ocean temperature signal during organic matter production. A shifted seasonality of the biological production in the LGM leads to a difference in the recording of the climate signal by this artificial tracer of up to 1 K. This could be of relevance for the interpretation of proxy records like, e.g., alkenones. Our study not only provides the first consistent insights into the biogeochemistry of the glacial Mediterranean Sea but will also serve as the starting point for transient simulations of the last deglaciation.

Tiny rain makers: How aerosols shape extreme rainfall simulation accuracy

Given the rapid spatiotemporal variability of aerosols and their complex impacts on the Earth system, investigating the role of aerosols in modelling extreme weather events, particularly various extreme rainfalls, remains limited. This study explored extreme rainfall simulation sensitivity to aerosol properties, transport and seasonality over the UK and Ireland during four seasons in 2020 by the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model. Two sets of high-resolution simulations, one including aerosol direct and indirect effects and the other one without any aerosol effects, were conducted to investigate the improvements due to aerosol inputs. Meteorological results were verified using ground and satellite observations to examine the reliability of simulations. The 12 extreme rainfall events during the study months were classified by their backward trajectories from the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, and their simulation accuracies were quantified through six spatiotemporal metrics and one overall score. By comprehensive analyses of the simulation performances under different conditions, it is found that the model performs better in simulating rainfall events driven by sea salt aerosols (SSA) compared to those promoted by anthropogenic aerosols. Air masses transported from the Arctic and North Atlantic Ocean also help achieve higher rainfall simulation accuracy than European air masses. Rainfall simulations for winter and autumn events outperform those for spring and summer events. When considering aerosol effects in simulations, almost half of the events showed improved performance, while an equal number experienced a decrease and a small proportion remained unchanged. The most remarkable improvements are observed in rainfall simulations with higher concentrations of SSA and the participation of anthropogenic aerosols, while simulations promoted only by anthropogenic aerosols all experience performance reductions. The aerosol effects also led to significant increases in monthly rainfalls, accompanied by great reductions of Cl in larger particle size aerosols and NO3 in smaller particle size aerosols.

Effects of the Emeishan large igneous province (ELIP) and coastal upwelling on paleoenvironment and organic matter accumulation during the Middle Permian (Capitanian), South China

The late Middle Permian (Capitanian) was characterized by many global geological, biotic and environmental events, along with the widespread deposition of organic-rich strata. Elucidating the relationships between organic matter (OM) accumulation and critical geological events is of significance for Earth system science and petroleum resource geology, but the mechanism remains unclear. To fill the knowledge gap, this study investigated the Gufeng Formation in the middle Yangtze region of South China to examine the links between OM accumulation and geological events, e.g., the Emeishan large igneous province (ELIP) and coastal upwelling. Our results show that the Gufeng Formation exhibits significant OM enrichment, with an average total organic carbon (TOC) content of 8.34 wt%. The paleoceanographic conditions went through three stages during the Capitanian: the early–middle stage of high primary productivity and anoxic–sulfidic conditions; the middle stage of moderate productivity and anoxic conditions; and the late stage of low productivity and dysoxic conditions due to weakened upwelling and a global sea-level fall. OM accumulation was influenced mainly by upwelling-induced high productivity and anoxic conditions related to the oxygen minimum zone (OMZ), with a secondary control of carbonate dilution. The ELIP possibly had a limited contribution to high TOC contents. In South China, the Gufeng and uppermost Maokou formations were marked by high OM contents (average TOC > 3 wt%; maximum = 40 wt%), resulting primarily from widespread coastal upwelling along the eastern margin of the Paleo-Tethys Ocean during the Middle Permian. ELIP volcanism mainly triggered rapid climate warming during the late Capitanian and had a regional and variable effect on OM enrichment for different regions in South China.

Contribution of internal variability to the Mongolian Plateau summer precipitation trends in MPI-ESM large-ensemble model

Summer precipitation over the Mongolian Plateau (MP) has experienced a consistent decline in recent decades. While the influence of atmospheric wave train on this reduction in precipitation has been recognized in prior studies, this study delves deeper into the physical mechanisms and quantifies the contributions of the internal atmosphere and oceanic variations to the diminishing precipitation utilizing a comprehensive 100-member ensemble simulations from the Max Planck Institute Earth System Model (MPI-ESM). Results show that the ensemble-mean precipitation in MP exhibits a positive trend and cannot explain the observed results. The precipitation trends vary significantly among individual ensemble members, highlighting the pivotal role of internal variability. The leading EOF mode of precipitation trends among ensemble members exhibits uniform variations. Further investigations reveal that the internal summer precipitation in MP is affected by the internal atmospheric circulation, the remote influence of the North Atlantic Dipole sea surface temperature (SST) anomalies, and Pacific Decadal Oscillation-like SST patterns. An eastward-propagating Rossby wave originating from the North Atlantic dipole SST anomalies provides the anomalous large-scale circulation that influences summer precipitation. The PDO contributes to reinforcing the anticyclonic anomaly over the MP. Additionally, the uncertainty of precipitation trends in MPI-ESM can be reduced by 13% through removing the internal atmospheric wave train-related precipitation variation, while oceanic factors only contribute about 7% uncertainty of precipitation variations. Our insights enhance the understanding of the physical drivers behind summer precipitation variability in the MP and effectively quantify the uncertainties stemming from internal variability.

The importance of natural land carbon sinks in modelling future emissions pathways and assessing individual country progress towards net-zero emissions targets

Nature-based solutions (NBS), in the form of active ecosystem conservation, restoration and improved land management, represent a pathway to accelerate net-zero emissions (NZE) strategies and support biodiversity. Meaningful implementation and successful accounting depend on the ability to differentiate between anthropogenic and natural carbon fluxes on land. The United Nations Framework Convention on Climate Change (UNFCCC) land carbon accounting methods currently incorporate all CO2 fluxes on managed land in country inventories without distinguishing between anthropogenic and natural components. Meanwhile, natural land carbon sinks are modelled by earth system models but are mostly reported at global level. Here we present a simple yet novel methodology to estimate the present and future progression of natural land sinks at the country and regional level. Forests dominate the uptake of CO2 on land and as such, our analysis is based on allocating global projections of the natural land carbon flux to individual countries using a compilation of forest land areas for a historic and scenario range spanning 1960–2100. Specifically, we use MIT’s carbon cycle model simulations that are set in the context of emissions pathways from the Shell Energy Security Scenarios (2023). Our natural land carbon flux estimates for individual countries and regions such as the European Union (EU) show generally good agreement with independent estimates from recent land-use harmonisation studies for 2000–2020. Hence, our approach may also provide a simple, first-order exploration of future natural land fluxes at country level—a potential that other studies do not yet offer. In turn, this enables better understanding of the anthropogenic and natural components contributing to country NZE targets under different scenarios. Nevertheless, our findings also suggest that models such as the Shell World Energy Model (WEM) would benefit from further improvements in the apportionment of land carbon sources and sinks to evaluate detailed actions to meet country targets. More importantly, uncertainties remain regarding the resilience of land ecosystems and their capacity to store increasing amounts of carbon under progressive global warming. Therefore, we recommend that the carbon cycle modelling and energy modelling research communities continue to collaborate to develop a next generation of relevant data products to distinguish anthropogenic from natural impacts at local, regional and national levels.

Compensating atmospheric adjustments reduce the volcanic forcing from Hunga stratospheric water vapor enhancement.

The 2022 eruption of the Hunga submarine volcano injected an unprecedented volume of water vapor into the stratosphere, presenting a unique, natural experiment for ascertaining the influence of stratospheric water vapor within the global radiation budget. This study examines the radiative forcings of the Hunga stratospheric water vapor enhancement, comparing stratosphere-adjusted radiative forcing derived from offline methods to an effective radiative forcing derived from Earth System Model simulations. Assuming a uniform 2 parts per million mass mixing ratio increase of water vapor in the Southern Hemisphere stratosphere, we estimated the instantaneous, stratosphere-adjusted, and overall effective radiative forcing to be -0.04, 0.08, and 0.05 W m-2, respectively. The lower magnitude of the positive volcanic stratospheric water vapor effective radiative forcing is due to compensating effects from atmospheric adjustments. Ensemble simulations of a coupled atmosphere-ocean model suggest a surface warming of 0.05 K, affirming a limited influence on global mean surface temperature from the volcanic stratospheric water vapor injection.

Biogeographic climate sensitivity controls Earth system response to large igneous province carbon degassing.

Periods of large igneous province (LIP) magmatism have shaped Earth's biological and climatic history, causing major climatic shifts and biological reorganizations. The vegetation response to LIP-induced perturbations may affect the efficiency of the carbon-climate regulation system and the post-LIP climate evolution. Using an eco-evolutionary vegetation model, we demonstrate here that the vegetation's climate adaptation capacity, through biological evolution and geographic dispersal, is a major determinant of the severity and longevity of LIP-induced hyperthermals and can promote the emergence of a new climatic steady state. Proxy-based temperature reconstructions of the Permian-Triassic, Triassic-Jurassic, and Paleocene-Eocene hyperthermals match the modeled trajectories of bioclimatic disturbance and recovery. We conclude that biological vegetation dynamics shape the multimillion-year Earth system response to sudden carbon degassing and global warming episodes.

Improving the Predictability of the US Seasonal Surface Temperature With Convolutional Neural Networks Trained on CESM2 LENS

AbstractTo better understand and improve the prediction of the seasonal surface temperature (TS) across the United States, we employed convolutional neural network (CNN) models trained on the Community Earth System Model Version 2 Large Ensemble (CESM2 LENS). We used lagged sea surface temperatures (SST) over the tropical Pacific region, containing the information of the El Niño Southern Oscillation (ENSO), as input for the CNN models. ENSO is the principal driver of variability in seasonal US surface temperatures (TSUS) and employing CNN models allows for spatiotemporal aspects of ENSO to be analyzed to make seasonal TSUS predictions. For predicting TSUS, the CNN models exhibited significantly improved skill over standard statistical multilinear regression (MLR) models and dynamical forecasts across most regions in the US, for lead times ranging from 1 to 6 months. Furthermore, we employed the CNN models to predict seasonal TSUS during extreme ENSO events. For these events, the CNN models outperformed the MLR models in predicting the effects on seasonal TSUS, suggesting that the CNN models are able to capture the ENSO‐TSUS teleconnection more effectively. Results from a heatmap analysis demonstrate that the CNN models utilize spatial features of ENSO rather than solely the magnitude of the ENSO events, indicating that the improved skill of seasonal TSUS is due to analyzing spatial variation in ENSO events. The proposed CNN model demonstrates a promising improvement in prediction skill compared to existing methods, suggesting a potential path forward for enhancing TSUS forecast skill from subseasonal to seasonal timescales.

Spatially Resolved Temperature Response Functions to CO2 Emissions

AbstractCarbon dioxide (CO2) emissions affect local temperature; quantifying that local response is important for learning about the earth system, the impacts of mitigation, and adaptation needs. We assume the climate system can be represented as a time‐dependent linear system, diagnosing Green's Functions for the spatial temperature response to CO2 emissions based on CMIP6 earth system models. This allows us to emulate the linear component of the temperature response to CO2. This approach is sufficient to capture the spatial temperature response of CMIP6 experiments within one standard deviation of the multimodel spread across most regions, though accuracy is lower in the Southern Ocean and the Arctic. Our approach reveals where nonlinear feedbacks are important in current CMIP6 models, and where the local system response is well represented by a time‐dependent linear differential operator. It incorporates emissions path dependency and may be useful for evaluating large ensembles of emission scenarios.

Nitrogen cycle module for INM RAS climate model

Abstract Nitrogen is one of the most abundant chemical elements on the Earth and plays an important role in global environmental change. Leading Earth system models include coupled carbon and nitrogen cycle modules of varying complexity, but the INM RAS climate model family has not yet included an explicit N-cycle description. This paper presents a parameterization of the terrestrial N-cycle based on a simplification of the JULES-CN model, adapted for coupled use with the INM-CM land C-cycle module. Numerical simulations were carried out with a standalone carbon cycle model with nitrogen feedback disabled and enabled versions for the period 1850–2100. The simulated global pools show good agreement with results of other models with an implemented N-cycle. Taking into account the N-limitation of the C-cycle, the modelled dynamics of total carbon storage in terrestrial ecosystems from 1850 to the mid-20th century is specified.

Seasonal predictions of summer compound humid heat extremes in the southeastern United States driven by sea surface temperatures

Humid heat extreme (HHE) is a type of compound extreme weather event that poses severe risks to human health. Skillful forecasts of HHE months in advance are crucial for developing strategies to enhance community resilience to extreme events1,2. This study demonstrates that the frequency of summertime HHE in the southeastern United States (SEUS) can be skillfully predicted 0–1 months in advance using the SPEAR (Seamless system for Prediction and EArth system Research) seasonal forecast system. Sea surface temperatures (SSTs) in the tropical North Atlantic (TNA) basin are identified as the primary driver of this prediction skill. The responses of large-scale atmospheric circulation and winds to anomalous warm SSTs in the TNA favor the transport of heat and moisture from the Gulf of Mexico to the SEUS. This research underscores the role of slowly varying sea surface conditions in modifying large-scale environments, thereby contributing to the skillful prediction of HHE in the SEUS. The results of this study have potential applications in the development of early warning systems for HHE.

Contrasting responses of vegetation productivity to intraseasonal rainfall in Earth system models

Abstract. Correctly representing the response of vegetation productivity to water availability in Earth system models (ESMs) is essential for accurately modelling the terrestrial carbon cycle and the evolution of the climate system. Previous studies evaluating gross primary productivity (GPP) in ESMs have focused on annual mean GPP and interannual variability, but physical processes at shorter timescales are important for determining vegetation–climate coupling. We evaluate GPP responses at the intraseasonal timescale in five CMIP6 ESMs by analysing changes in GPP after intraseasonal rainfall events with a timescale of approximately 25 d. We compare these responses to those found in a range of observation-based products. When composited around all intraseasonal rainfall events globally, both the amplitude and the timing of the GPP response show large inter-model differences, demonstrating discrepancies between models in their representation of water–carbon coupling processes. However, the responses calculated from the observational datasets also vary considerably, making it challenging to assess the realism of the modelled GPP responses. The models correctly capture the fact that larger increases in GPP at the regional scale are associated with larger increases in surface soil moisture and larger decreases in atmospheric vapour pressure deficit. However, the sensitivity of the GPP response to these drivers varies between models. The GPP in NorESM is insufficiently sensitive to vapour pressure deficit perturbations when compared all to other models and six out of seven observational GPP products tested. Most models produce a faster GPP response where the surface soil moisture perturbation is larger, but the observational evidence for this relationship is weak. This work demonstrates the need for a better understanding of the uncertainties in the representation of water–vegetation relationships in ESMs and highlights a requirement for future daily-resolution observations of GPP to provide a tighter constraint on global water–carbon coupling processes.

Evolution Characteristics of Convective Clouds With Relatively Small Scales Over South China

AbstractAs a crucial element in the Earth's system, development of convective clouds is still insufficiently understood and simulated in both weather and climate models, particularly for small‐scale regional convective clouds. In this study, a series of convective clouds cases with relatively small scales are selected over south China, and the evolution characteristics of those convective clouds are investigated using high spatiotemporal resolution geostationary satellite data. Statistical results show that the shorter the life cycle or the smaller the area, the higher the proportion of convective clouds. Notably, approximately 79.23% of convective clouds have a life cycle of less than 3 hr, and 63.81% have an area of less than 500 km2 for selected cases. In addition, there are significant differences in the cloud characteristics and meteorological parameters during various convective cloud stages and durations. Nevertheless, the relative proportions of convective clouds at three identified stages remain relatively constant with almost no dependence on duration of convective clouds, which are 33.50%, 23.92%, and 42.58% for the developing, mature, and dissipation stages, respectively. In addition, we find that the cloud‐top cooling rate during the developing stage also affects the characteristics of the later stage of convective clouds. Quantitatively, the average cloud area and duration changed by 157.03 km2 and 0.17 hr when the cloud‐top cooling rate varies by 15 K/h.

Exploration of diverse solutions for the calibration of imperfect climate models

Abstract. The calibration of Earth system model parameters is subject to data, time, and computational constraints. The high dimensionality of this calibration problem, combined with errors arising from model structural assumptions, makes it impossible to find model versions fully consistent with historical observations. Therefore, the potential for multiple plausible configurations presenting different trade-offs between skills in various variables and spatial regions remains usually untested. In this study, we lay out a formalism for making different assumptions about how ensemble variability in a perturbed physics ensemble relates to model error, proposing an empirical but practical solution for finding diverse near-optimal solutions. A meta-model is used to predict the outputs of a climate model reduced through principal component analysis. Then, a subset of input parameter values yielding results similar to a reference simulation is identified. We argue that the effective degrees of freedom in the model performance response to parameter input (the “parametric component”) are, in fact, relatively small, illustrating why manual calibration is often able to find near-optimal solutions. The results explore the potential for comparably performing parameter configurations that have different trade-offs in model errors. These model candidates can inform model development and could potentially lead to significantly different future climate evolution.

Interpretable foundation models as decryptors peering into the Earth system

Interpretable foundation models as decryptors peering into the Earth system

A large net carbon loss attributed to anthropogenic and natural disturbances in the Amazon Arc of Deforestation

The Amazon forest contains globally important carbon stocks, but in recent years, atmospheric measurements suggest that it has been releasing more carbon than it has absorbed because of deforestation and forest degradation. Accurately attributing the sources of carbon loss to forest degradation and natural disturbances remains a challenge because of the difficulty of classifying disturbances and simultaneously estimating carbon changes. We used a unique, randomized, repeated, very high-resolution airborne laser scanning survey to provide a direct, detailed, and high-resolution partitioning of aboveground carbon gains and losses in the Brazilian Arc of Deforestation. Our analysis revealed that disturbances directly attributed to human activity impacted 4.2% of the survey area while windthrows and other disturbances affected 2.7% and 14.7%, respectively. Extrapolating the lidar-based statistics to the study area (544,300 km2), we found that 24.1, 24.2, and 14.5 Tg C y-1 were lost through clearing, fires, and logging, respectively. The losses due to large windthrows (21.5 Tg C y-1) and other disturbances (50.3 Tg C y-1) were partially counterbalanced by forest growth (44.1 Tg C y-1). Our high-resolution estimates demonstrated a greater loss of carbon through forest degradation than through deforestation and a net loss of carbon of 90.5 ± 16.6 Tg C y-1 for the study region attributable to both anthropogenic and natural processes. This study highlights the role of forest degradation in the carbon balance for this critical region in the Earth system.

Assessing the Impacts of Falling Ice Radiative Effects on the Seasonal Variation of Land Surface Properties

AbstractThe impacts of falling ice radiative effects (FIREs) on land‐atmosphere feedback processes were examined, with a focus on the fidelity of land surface properties and their variability as inferred by global climate models (GCMs). We conducted a pair of sensitivity experiments using the National Center for Atmospheric Research (NCAR) Community Earth System Model Version 1 (CESM1) in fully coupled modes with FIREs turned on and off. This allowed us to investigate the seasonal response of land surface properties to changes in radiation fluxes and land surface temperature (LST) associated with FIREs across global land areas. Our findings indicate that during boreal winter, excluding FIREs results in less surface downward longwave and net flux (∼5–15 Wm−2), leading to a colder land surface (∼2–4 K) and air temperatures (∼1–4 K) at mid‐ and high latitudes. Consequently, the surface frozen soil layer and snow cover persist through spring, delaying snowmelt and thawing until summer. This delay reduces liquid soil moisture, thereby suppressing vegetation productivity in subsequent seasons. Conversely, tropical regions, exhibit contrasting responses, with a warmer land surface (∼0.5 K) and warmer air temperatures (∼0.1–0.5 K) due to increased surface downward shortwave and net flux (∼2–10 Wm−2). This enhancement in radiation fosters increased vegetation productivity throughout the seasonal cycle. These findings illustrate a local response of land surface properties to changes in the surface energy balance and LST, highlighting the significant role that FIREs play in land surface modeling within GCMs.

PT-ESM: A Parameter-Testing and Integration Framework for Earth System Models Oriented towards High-Performance Computing

High-performance computing (HPC) plays a crucial role in scientific computing, and the efficient utilization of HPC to accomplish computational tasks remains a focal point of research. This study addresses the issue of parameter tuning for Earth system models by proposing a comprehensive solution based on the concept of scientific workflows. This solution encompasses detailed methods from sensitivity analysis to parameter tuning and incorporates various approaches to enhance result accuracy. We validated the reliability of our methods using five cases in the Single Column Atmosphere Model (SCAM). Specifically, we investigated the influence of fluctuations of 11 typical parameters on 10 output variables. The experimental results show that the magnitude of the impact on the results varies significantly when different parameters are perturbed. These findings will help researchers develop more reasonable parameterization schemes for different regions and seasons.