Climate Model Simulations Research Articles

Leveraging machine learning for environmental policy innovation: Advances in Data Analytics to address urban and ecological challenges

The intersection of machine learning and environmental policy offers transformative potential in addressing pressing urban and ecological challenges such as climate change, pollution, and sustainable development. This study examines state-of-the-art machine learning techniques utilized in environmental data modeling, including predictive analytics, neural networks, and geospatial analysis, to uncover patterns, forecast trends, and optimize resource allocation. By leveraging vast datasets from urban environments and natural ecosystems, machine learning tools provide actionable insights to guide environmental policy and drive impactful interventions. This paper proposes an interdisciplinary framework that integrates machine learning with social, economic, and environmental sciences to inform policymaking. The framework emphasizes three primary objectives: promoting urban sustainability, addressing ecological disparities, and mitigating the effects of environmental hazards. Machine learning applications such as air quality prediction, climate resilience modeling, and waste management optimization are discussed as pivotal tools for achieving these goals. Furthermore, the study highlights the role of ethical data governance and inclusivity in ensuring equitable outcomes, particularly for vulnerable populations disproportionately affected by environmental degradation. Emerging trends in machine learning, including real-time monitoring through IoT devices, autonomous systems for ecological conservation, and advanced climate simulation models, are reviewed to underscore their potential for driving innovative policy solutions. The study also identifies challenges such as data bias, algorithm transparency, and resource limitations, proposing collaborative strategies to overcome these barriers. The findings advocate for a paradigm shift in environmental policy development, where data-driven, machine-learning-enabled approaches complement traditional methods. This integration fosters more dynamic, evidence-based decision-making processes capable of adapting to rapidly evolving environmental conditions. By addressing urban and ecological challenges holistically, machine learning can serve as a catalyst for sustainable development and global environmental equity. Keywords: Machine Learning, Environmental Policy, Urban Sustainability, Ecological Challenges, Climate Change, Data Analytics, Geospatial Analysis, Predictive Modeling, Sustainable Development, Ethical Data Governance.

Paleoclimate proxy records suggest reduced tropical Pacific zonal asymmetry under sustained global warming

Pronounced model-observation discrepancies in the changes of tropical Pacific zonal sea surface temperature gradient during the satellite era imply systematic model deficiencies. However, the relatively short high-quality instrumental record hampers robustly determining the response of tropical Pacific sea surface temperature to greenhouse gas increases. By adopting paleoclimate proxy records along with a series of climate model simulations, here we show that the zonal gradient is likely to decrease under sustained strong forcing. Paleoclimate proxy records indicate an overall increase of the zonal gradient over time, which has been accompanied by global-mean cooling associated with decreasing carbon dioxide concentrations. Model simulations are found to broadly reproduce the distinct contrast between warmer high carbon dioxide climates and the opposite climates, albeit with large inter-model discrepancy. The qualitative agreement among paleoclimate proxy records and modeled representations therefore lends some important credence to the sign of model-projected future tropical Pacific mean state change.

The 1,000-year context of extreme precipitation in the Central US from a novel blend of observations and climate model simulations

The 1,000-year context of extreme precipitation in the Central US from a novel blend of observations and climate model simulations

Asynchronous Poleward Migration of the Atlantic Subtropical Gyres Over the Past 22,000 years

AbstractBy exchanging huge amounts of heat between the tropics and high latitudes, subtropical gyres significantly impact Earth's energy balance. Yet, their dynamical changes during the last deglaciation remain poorly understood. Here, nine records of the planktonic foraminiferal species Globorotalia truncatulinoides, that inhabits the permanent deep thermocline of subtropical gyres, are used to explore the meridional migration of both the North and South Atlantic subtropical gyres (NASG and SASG, respectively) in the past 22,000 years. We find that both gyres migrated poleward, with the SASG migration 1,500 years earlier than the NASG. Records from the North Atlantic Ocean indicate that the NASG's northern boundary has shifted over 6°. Climate model simulations suggest that these migrations are coupled with shifts in meridional temperature gradients. The poleward migration of the Atlantic subtropical gyres was crucial for sustaining a milder modern high‐latitude climate in comparison with that of the last ice age.

Precession modulates the poleward expansion of atmospheric circulation to the Arctic Ocean

Under sustained global warming, Arctic climate is projected to become more responsive to changes in North Pacific meridional heat transport as a result of teleconnections between low and high latitudes, but the underlying mechanisms remain poorly understood. Here, we reconstruct subarctic humidity changes over the past 400 kyr to investigate the role of low-to-high latitude interactions in regulating Arctic hydroclimate. Our reconstruction is based on precipitation-driven sediment input variations in the Subarctic North Pacific (SANP), which reveal a strong precessional cycle in subarctic humidity under the relatively low eccentricity variations that dominated the past four glacial-interglacial cycles. Combined with climate model simulations, we highlight that precession drives meridional shifts in the northern rim of the North Pacific Subtropical Gyre (NPSG) and modulates the efficiency of heat and water vapor transfer to the SANP and Arctic regions. Our findings suggest that projections of a northward shift of the NPSG in response to future global warming will lead to wetter conditions in the Arctic Ocean and enhanced sea-ice loss.

Causes of growing middle-to-upper tropospheric ozone over the northwest Pacific region

Abstract. Long-term ozone (O3) changes in the middle-to-upper troposphere are critical to climate radiative forcing and tropospheric O3 pollution. Yet, these changes remain poorly quantified through observations in East Asia. Concerns also persist regarding the data quality of the ozonesondes available at the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) for this region. This study aims to address these gaps by analyzing O3 soundings at four sites along the northwestern Pacific coastal region over the past 3 decades and by assessing their consistency with an atmospheric chemistry–climate model simulation. Utilizing the European Centre for Medium-Range Weather Forecasts (ECMWF) Hamburg (ECHAM)/Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) nudged simulations, it is demonstrated that trends between model and ozonesonde measurements are overall consistent, thereby gaining confidence in the model's ability to simulate O3 trends and confirming the utility of potentially imperfect observational data. A notable increase in O3 mixing ratio around 0.29–0.82 ppb a−1 extending from the middle troposphere to the upper troposphere is observed in both observations and model simulations between 1990 and 2020, primarily during spring and summer. The timing of these O3 tongues is delayed when moving from south to north along the measurement sites, transitioning from late spring to summer. Investigation into the drivers of these trends using tagged model tracers reveals that O3 of stratospheric origin (O3S) dominates the absolute O3 mixing ratios over the middle-to-upper troposphere in the subtropics, contributing to the observed O3 increases by up to 96 % (40 %) during winter (summer), whereas O3 of tropospheric origin (O3T) governs the absolute value throughout the tropical troposphere and contributes generally much more than 60 % to the positive O3 changes, especially during summer and autumn. During winter and spring, a decrease in O3S is partly counterbalanced by an increase in O3T in the tropical troposphere. This study highlights that the enhanced downward transport of stratospheric O3 into the troposphere in the subtropics and a surge of tropospheric O3 in the tropics are the two key factors driving the enhancement of O3 in the middle-to-upper troposphere along the northwest Pacific region.

Sub-Daily Performance of a Convection-Permitting Model in Simulating Decade-Long Precipitation over Northwestern Türkiye

One of the main differences between regional climate model and convection-permitting model simulations is not just how well topographic characteristics are represented, but also how deep convection is treated. The convection process frequently occurs within hours, thus a sub-daily scale becomes appropriate to evaluate these changes. To do this, a series of simulations has been carried out at different spatial resolutions (0.11 and 0.025) using the COSMO-CLM (CCLM) climate model forced by the ECMWF Reanalysis v5 (ERA5) between 2011 and 2020 over a domain covering northwestern Türkiye. Hourly precipitation and heavy precipitation simulated by both models were compared with the observations by Turkish State Meteorological Service (TSMS) stations and Integrated Multi-satellitE Retrievals for GPM (IMERG). Subsequently, we aimed to identify the reasons behind these differences by computing several atmospheric stability parameters and conducting event-scale analysis using atmospheric sounding data. CCLM12 displays notable discrepancies in the timing of the diurnal cycle, exhibiting a premature shift of several hours when compared to the TSMS. CCLM2.5 offers an accurate representation of the peak times, considering all hours and especially those occurring during the wet hours of the warm season. Despite this, there is a tendency for peak intensities to be overestimated. In both seasons, intensity and extreme precipitation are highly underestimated by CCLM12 compared to IMERG. In terms of statistical metrics, the CCLM2.5 model performs better than the CCLM12 model under extreme precipitation conditions. The comparison between CCLM12 and CCLM2.5 at 12:00 UTC reveals differences in atmospheric conditions, with CCLM12 being wetter and colder in the lower troposphere but warmer at higher altitudes, overestimating low-level clouds and producing lower TTI and KI values. These conditions can promote faster air saturation in CCLM12, resulting in lower LCL and CCL, which foster the development of low-level clouds and frequent low-intensity precipitation. In contrast, the simulation of higher TTI and KI values and a steeper lapse rate in CCLM2.5 enables air parcels to enhance instability, reach the LFC more rapidly, increase EL, and finally promote deeper convection, as evidenced by higher CAPE values and intense low-frequency precipitation.

Impact of Grid Resolution on Wave‐Mean Flow Interactions With High Resolution Mars Global Climate Model Simulations

AbstractWe have executed a series of simulations with the NASA Ames Mars Global Climate Model by systematically increasing horizontal and vertical resolution with the goal of resolving small‐scale waves that significantly affect the mean thermal and momentum structure of the atmosphere. Our reference high‐resolution simulation is 1/4° horizontal resolution and ∼1.5 km vertical resolution, and we compare results to a low‐resolution simulation that is ∼4° in the horizontal and ∼4 km in the vertical. We show that the main biases in the zonal mean temperature and momentum fields between the low‐ and high‐resolution simulations can be alleviated by including parameterized orographic and non‐orographic gravity waves at low resolution.

Assessment of Regional Climate Model simulations at reproducing the link between PDO and Climate Extreme Precipitation indices in Mexico

Assessment of Regional Climate Model simulations at reproducing the link between PDO and Climate Extreme Precipitation indices in Mexico

Linking Radiative‐Advective Equilibrium Regime Transition to Arctic Amplification

AbstractEmission of anthropogenic greenhouse gases has resulted in greater Arctic warming compared to global warming, known as Arctic amplification (AA). From an energy‐balance perspective, the current Arctic climate is in radiative‐advective equilibrium (RAE) regime, in which radiative cooling is balanced by advective heat flux convergence. Exploiting a suite of climate model simulations with varying carbon dioxide () concentrations, we link the northern high‐latitude regime variation and transition to AA. The dominance of RAE regime in northern high‐latitudes under reduction relates to stronger AA, whereas the RAE regime transition to non‐RAE regime under increase corresponds to a weaker AA. Examinations on the spatial and seasonal structures reveal that lapse‐rate and sea‐ice processes are crucial mechanisms. Our findings suggest that if concentration continues to rise, the Arctic could transition into a non‐RAE regime accompanied with a weaker AA.

Cause and Characteristics of Changes in Mesoscale Convective Systems within a Convection-Permitting Regional Climate Model

Abstract Changes in the frequency and intensity of mesoscale convective systems (MCSs) are assessed using convection-permitting regional climate model simulations. We present a novel classification method that relates MCSs to the magnitude of their large-scale forcing environments to better understand the changing nature of MCSs in a possible future climate scenario. Overall, the annual frequency, intensity, and amount of precipitation associated with MCSs are projected to increase for broad portions of the eastern conterminous United States (CONUS). Furthermore, changes in the characteristics of MCSs show larger, longer-lived, and faster MCSs particularly over the Midwest and Southeast. Seasonal examination of this response reveals a robust intensification of March–May (MAM) MCSs. The higher frequency of MAM MCSs is found to occur in weakly forced synoptic environments. Increased rainfall amounts are explained by an intensification of MCSs due to changing thermodynamics and enhanced lower-tropospheric moisture transport into the central CONUS by the North Atlantic subtropical high. Differences in midlatitude storm tracks are favorable toward the enhanced development of MCSs associated with strong baroclinic forcing within the Midwest and northern plains but are only realized in the presence of strong changes in the lower-tropospheric moisture transport. Overall, these results suggest a shift in the behavior of MAM MCSs that more closely corresponds to the behavior of June–August (JJA) MCSs in the historical climate. The increased occurrence of MCSs in weakly forced environments, particularly in MAM, deserves further investigation. Significance Statement Mesoscale convective systems (MCSs) make large contributions to the annual precipitation in the central United States, but we have a limited understanding as to how they may change in a warmer climate. Using a sophisticated tracking algorithm in conjunction with output from a high-resolution regional climate model, we find that MCSs will become more intense overall and occur more frequently in spring in the eastern United States. This frequency increase is strongly supported by a greater occurrence of MCSs in environments lacking support from large-scale weather systems. Instead, simulated changes in persistent large-scale atmospheric features facilitate the climatological transport of warmer and moister air into the eastern United States in spring, supporting an enhanced MCS response despite statistically insignificant changes in the frequency of individual weather systems.

Sustainability of regional Antarctic ice sheets under late Eocene seasonal atmospheric conditions

Abstract. The Eocene–Oligocene transition (EOT) is marked by a sudden δ18O excursion occurring in two distinct phases approximately 500 kyr apart. These phases signal a shift from the warm middle to late Eocene greenhouse climate to cooler conditions, with global surface air temperatures decreasing by 3–5 °C and the emergence of the first continent-wide Antarctic ice sheet (AIS). While ice sheet modelling suggests that ice sheet growth can be triggered by declining pCO2, it remains unclear how this transition was initiated, particularly the first growth phase that appears to be related to oceanic and atmospheric cooling rather than ice sheet growth. Recent climate model simulations of the late Eocene show improved accuracy but depict climatic conditions that are not conducive to the survival of incipient ice sheets throughout the summer season. This study therefore examines whether it is plausible to develop ice sheets of sufficient scale to trigger the feedback mechanisms required to disrupt the atmospheric regime above the Antarctic continent during warm and moist late Eocene summers and establish more favourable conditions for ice expansion. We aim to assess the sustainability of an incipient AIS under varying radiative, orbital and cryospheric forcing. To do so, we evaluate Community Earth System Model 1.0.5 simulations, using a 38 Ma geographical and topographical reconstruction, considering different radiative and orbital forcings. The climatic conditions prevailing during (and leading up to) the EOT can be characterised as extremely seasonal and monsoon-like, featuring a short yet intense summer period and contrasting cold winters. A narrow convergence zone with moist convection around the region with high sub-cloud equivalent potential temperature exhibits a ring-like structure, advecting moist surface air from the Southern Ocean in both summer and winter. This advection leads to high values of moist static energy and subsequent precipitation in coastal regions. Paradoxically, this atmospheric regime – particularly its coastal precipitation in winter – appears to be necessary for the sustenance of the moderately sized regional ice sheets we imposed on the continent, contrary to our assumption that these ice sheets would disrupt the atmospheric regime. This underscores a hysteresis effect for regional ice sheets on the Antarctic continent, suggesting the potential for a significant volume of ice on the continent without imminent full glaciation prior to the EOT.

Downscaling the probability of heavy rainfall over the Nordic countries

Abstract. We used empirical–statistical downscaling to derive local statistics for 24 h and sub-daily precipitation over the Nordic countries, based on large-scale information provided by global climate models. The local statistics included probabilities for heavy precipitation and intensity–duration–frequency (IDF) curves for sub-daily rainfall. The downscaling was based on estimating key parameters defining the shape of mathematical curves describing probabilities and return values, namely the annual wet-day frequency, fw, and the wet-day mean precipitation, μ. Both parameters were used as predictands representing local precipitation statistics as well as predictors representing large-scale conditions. We used multi-model ensembles of global climate model (CMIP6) simulations, calibrated on the ERA5 reanalysis, to derive local projections and future outlooks. Our analysis included an evaluation of how well the global climate models reproduced the predictors in addition to assessing the quality of downscaled precipitation statistics. The evaluation suggested that present global climate models capture essential aspects of the covariance, and there was a good match between annual wet-day frequency and wet-day mean precipitation derived from ERA5 on the one hand and local rain gauges in the Nordic region on the other. Furthermore, the ensemble downscaled results for annual fw and μ were approximately normally distributed, which may justify using the ensemble mean and standard deviation to describe the ensemble spread. Hence, our efforts provide a demonstration for how empirical–statistical downscaling can be used to provide practical information on heavy rainfall, which subsequently may be used for impact studies. Future projections for the Nordic region indicated little increase in precipitation due to more wet days, but most of the contribution comes from increased mean intensity. The west coast of Norway had the highest probabilities of receiving more than 30 mm d−1 precipitation, but the strongest relative trend in this probability was projected over northern Finland. Furthermore, the highest estimates for trends in 10-year and 25-year return values were projected over western Norway, where they were high from the outset. Our results also suggested that future precipitation intensity is sensitive to future emissions, whereas the wet-day frequency is less sensitive.

Large-scale drivers of widespread extreme precipitation in Europe: insights from high-resolution regional climate model simulations

Large-scale drivers of widespread extreme precipitation in Europe: insights from high-resolution regional climate model simulations

Low Cloud–SST Variability over the Summertime Subtropical Northeast Pacific: Role of Extratropical Atmospheric Modes

Abstract Over the subtropical Northeast Pacific (NEP), highly reflective low clouds interact with underlying sea surface temperature (SST) to constitute a local positive feedback. Recent modeling studies showed that, together with wind–evaporation–SST (WES) feedback, the summertime low cloud–SST feedback promotes nonlocal trade wind variations, modulating subsequent evolution of El Niño–Southern Oscillation (ENSO). This study aims to identify drivers of summertime low-cloud variations, using satellite observations and global atmosphere model simulations forced with observed SST. A transbasin teleconnection is identified, where the north tropical Atlantic (NTA) warming induced by the North Atlantic Oscillation (NAO) increases precipitation, exciting warm Rossby waves that extend into the NEP. The resultant enhancement of static stability promotes summertime low cloud–SST variability. By regressing out the effects of the preceding ENSO and NTA SST, atmospheric internal variability over the extratropical North Pacific, including the North Pacific Oscillation (NPO), is found to drive the NEP cooling by latent heat loss and subsequent summer low cloud–SST variability. With the help of the background trade winds and WES feedback, the SST anomalies extend southwestward from the low-cloud region, accompanied by ENSO in the following winter. This suggests the nonlocal effects of low clouds identified by recent studies. Analysis of a 500-yr climate model simulation corroborates the NTA and NPO forcing of NEP low cloud–SST variability and subsequent ENSO.

Dynamic treeline and cryosphere response to pronounced mid-Holocene climatic variability in the US Rocky Mountains

Climate-driven changes in high-elevation forest distribution and reductions in snow and ice cover have major implications for ecosystems and global water security. In the Greater Yellowstone Ecosystem of the Rocky Mountains (United States), recent melting of a high-elevation (3,091 m asl) ice patch exposed a mature stand of whitebark pine (Pinus albicaulis) trees, located ~180 m in elevation above modern treeline, that date to the mid-Holocene (c. 5,950 to 5,440 cal y BP). Here, we used this subfossil wood record to develop tree-ring-based temperature estimates for the upper-elevation climate conditions that resulted in ancient forest establishment and growth and the subsequent regional ice-patch growth and downslope shift of treeline. Results suggest that mid-Holocene forest establishment and growth occurred under warm-season (May-Oct) mean temperatures of 6.2 °C (±0.2 °C), until a multicentury cooling anomaly suppressed temperatures below 5.8 °C, resulting in stand mortality by c. 5,440 y BP. Transient climate model simulations indicate that regional cooling was driven by changes in summer insolation and Northern Hemisphere volcanism. The initial cooling event was followed centuries later (c. 5,100 y BP) by sustained Icelandic volcanic eruptions that forced a centennial-scale 1.0 °C summer cooling anomaly and led to rapid ice-patch growth and preservation of the trees. With recent warming (c. 2000-2020 CE), warm-season temperatures now equal and will soon exceed those of the mid-Holocene period of high treeline. It is likely that perennial ice cover will again disappear from the region, and treeline may expand upslope so long as plant-available moisture and disturbance are not limiting.

Robust increase in South Asian monsoon rainfall under warming driven by extratropical clouds and ocean

The responses of South Asian Monsoon (SAM) circulation under global warming are known to be highly uncertain, leading to the widespread of SAM rainfall projections among models. Here, we show that the uncertain SAM circulation in Coupled Model Intercomparison Project Phase 6 models consists of two robust components that partly offset each other: a weakening component linked to a global thermodynamic constraint and a northward shift component understood through a regional 2D energetic perspective. We further attribute the robust northward shift of SAM circulation to positive cloud feedback over the Eurasia Continent and heat uptake in the Southern Ocean. A set of climate model simulations supports the finding that SAM rainfall increase is primarily due to the northward shift of circulation driven by extratropical processes. This energetic perspective opens new avenues for predicting monsoon rainfall by connecting circulation changes to radiative forcing, feedbacks, and ocean heat uptake.

An improved empirical quantile mapping approach for bias correction of extreme values in climate model simulations

Abstract Quantifying and correcting biases in modeling simulations is crucial for deriving meaningful findings across various scientific disciplines. Climate model simulations, in particular, often exhibit systemic biases when compared to observations. These biases may persist in future climate simulations, affecting the results of many climate change impact assessment studies. Empirical Quantile Mapping (QM) is a widely used method to correct such biases, mapping quantiles between observed and simulated Cumulative Distribution Functions (CDFs). However, empirical QM faces a fundamental challenge when the CDF of future simulations differs from historical simulations, potentially leading to extreme values falling outside the historical CDF range. To address this issue, our study introduces a novel approach to extrapolate future extreme values for bias correction, preserving the rank order of simulated future extremes. By construction, our approach ensures that bias corrected values are not exaggerated and retain the rank structure of the original simulated data, while preserving climate change signals in the bias corrected outputs. Additionally, our approach includes a technique to adjust the wet-day frequency for precipitation by preserving the ratio of wet-day frequency between observations and historical model simulations.

Assessing the Spurious Impacts of Ice-Constraining Methods on the Climate Response to Sea Ice Loss Using an Idealized Aquaplanet GCM

Abstract Coupled climate model simulations designed to isolate the effects of Arctic sea ice loss often apply artificial heating, either directly to the ice or through modification of the surface albedo, to constrain sea ice in the absence of other forcings. Recent work has shown that this approach may lead to an overestimation of the climate response to sea ice loss. In this study, we assess the spurious impacts of ice-constraining methods on the climate of an idealized aquaplanet general circulation model (GCM) with thermodynamic sea ice. The true effect of sea ice loss in this model is isolated by inducing ice loss through reduction of the freezing point of water, which does not require additional energy input. We compare the results of freezing point modification experiments with experiments where sea ice loss is induced using traditional ice-constraining methods and confirm the result of previous work that traditional methods induce spurious additional warming. Furthermore, additional warming leads to an overestimation of the circulation response to sea ice loss, which involves a weakening of the zonal wind and storm-track activity in midlatitudes. Our results suggest that coupled model simulations with constrained sea ice should be treated with caution, especially in boreal summer, where the true effect of sea ice loss is weakest, but we find the largest spurious response. Given that our results may be sensitive to the simplicity of the model we use, we suggest that devising methods to quantify the spurious effects of ice-constraining methods in more sophisticated models should be an urgent priority for future work. Significance Statement The potential effects of Arctic sea ice loss on midlatitude weather have been investigated using climate models where sea ice loss is artificially induced, without increasing greenhouse gas concentrations. Recently, this approach has been challenged because the artificial heating used to melt sea ice may also have direct effects on climate, which are not caused by sea ice loss. We run simulations with a simplified climate model that allows us to separate the “spurious” direct effects of the artificial heating from the “true” effect of sea ice loss. In our simulations, the responses of temperature and atmospheric circulation are amplified by spurious effects. Consequently, we argue that previous studies using more complex climate models may overestimate the effect of sea ice loss on climate.

An innovative approach to predicting global warming without using climate model simulations.

An innovative approach to predicting global warming without using climate model simulations.