Climate System Research Articles

Assessment of aerosol-cloud interactions over the Northern Indian Ocean

The interaction between aerosol and cloud causes one of the largest uncertainties in external forcing to our climate system, highlighting the importance of additional extensive research on aerosol – cloud interaction (ACI). Thus, in this study nearly two-decades (2000−2021) of Moderate Resolution Imaging Spectroradiometer (MODIS) derived aerosol and cloud dataset have been analysed to examine the warm cloud sensitivity to aerosols over the Northern Indian Ocean (NIO). We found a statistically significant anti-Twomey effect in warm clouds for lower liquid water path (< 70 gm−2) throughout all seasons, except for a pronounced Twomey effect during the monsoon season in the northern region. Nonetheless, limitations in satellite retrievals of cloud droplets prevent definitive conclusions on cloud droplet sensitivity. On other hand, albedo susceptibility indicates existence of cloud brightening in cleaner equatorial oceanic regions. Our results emphasize the importance of considering the distinctness of ACI at regional scale in global climate models which is essential for effectively constraining associated forcing mechanisms and enhancing the reliability of future predictions.

1500-Year Sedimentary Records of the East Asian Summer Monsoon and Yellow Sea Warm Current from the Muddy Area of the North Yellow Sea, China

Advances in reconstructing the East Asian monsoon have provided important insights into the natural climate variability in Asia during the pre-instrumental period. However, there are still unresolved paleoclimate issues that necessitate the use of geological proxy data to further our understanding of past climate changes. This study focused on core B13, located in the muddy area of the North Yellow Sea (NYS), to investigate the evolutionary history over the past 1500 years and reconstruct the records of the East Asian summer monsoon (EASM) and Yellow Sea warm current (YSWC). The mean grain size of sediment ranged from 4.2 Φ to 5.6 Φ, with the sorting coefficient ranging from 1.9 to 2.2, indicating poor sorting. The C–M pattern showed a limited range of values, with the M values being between 33 and 83 μm and the C values being between 165 and 287 μm, suggesting uniform-suspension transport. The L* index ranged from 40.41 to 44.12, while the a* and b* indexes ranged from 0.55 to 1.78 and 2.86 to 5.94, respectively. A stable and relatively strong sedimentary environment is indicated through a comprehensive analysis of the C–M plot, triangular plot, the relationship between the mean grain size and sorting, and the changes in grain-size and color parameters. The sedimentary evolution in the muddy area of the NYS over the past 1500 years can be categorized into three distinct stages. In this study, proxies for the EASM and YSWC were extracted using the VPCA method from the sediment grain size and diffuse spectral reflectance (DSR) data, respectively. The reliability of these proxies has been confirmed through comparison with other validated proxies. The results indicated that the strength of the EASM and YSWC also exhibited three stages, corresponding to the Dark Ages Cold Period (DACP), Medieval Warm Period (MWP), and Little Ice Age (LIA), respectively. On a centennial scale, the correlation between the EASM and YSWC was predominantly negative. This research validates the reliability of the VPCA method for paleoclimate reconstruction, contributes important climate records in a special muddy area, and provides a new perspective on how to eliminate temporal errors in verifying the correlation between the two climate systems.

Thermal performance of solar ground source heat pump system for rural buildings in Chinese cold zone

In response to China's national policy of “achieving carbon peak by 2030 and carbon neutrality by 2060″, and to promote the heating system of rural buildings in China's severe cold zone. Two heating schemes of the GSHP (Ground Source Heat Pump) system and the SGSHP (Solar Ground Source Heat Pump) system are designed and simulated for a single rural residential building in Harbin city. The results present that the outlet temperature of buried pipe in SGSHP system is 13.86 % higher than that in GSHP system, and coefficient of performance (COP) correspondingly increases 9.2 %. There is a better comprehensive performance for the SGSHP system in cold climates. The standard coal consumption of SGSHP system is about 72.35 % less than that of biomass boiler system, showing a greater energy efficiency. Compared to the local conventional biomass boiler system, the dynamic cost payback period of the SGSHP system amounts to 11 years, while yielding a net present value (NPV) of 10,800 CNY throughout the project's life cycle. The emissions of CO2, SO2, and dust are reduced by 2617.13 kg, 19.38 kg, and 14.535 kg respectively. The results indicate a great overall performance of the SGSHP system for residential heating in the cold climate zone. This research offers an energy efficient and eco-friendly alternative for the space heating of Chinese rural buildings in cold climates.

Characterizing climate pathways using feature importance on echo state networks

AbstractThe 2022 National Defense Strategy of the United States listed climate change as a serious threat to national security. Climate intervention methods, such as stratospheric aerosol injection, have been proposed as mitigation strategies, but the downstream effects of such actions on a complex climate system are not well understood. The development of algorithmic techniques for quantifying relationships between source and impact variables related to a climate event (i.e., a climate pathway) would help inform policy decisions. Data‐driven deep learning models have become powerful tools for modeling highly nonlinear relationships and may provide a route to characterize climate variable relationships. In this paper, we explore the use of an echo state network (ESN) for characterizing climate pathways. ESNs are a computationally efficient neural network variation designed for temporal data, and recent work proposes ESNs as a useful tool for forecasting spatiotemporal climate data. However, ESNs are noninterpretable black‐box models along with other neural networks. The lack of model transparency poses a hurdle for understanding variable relationships. We address this issue by developing feature importance methods for ESNs in the context of spatiotemporal data to quantify variable relationships captured by the model. We conduct a simulation study to assess and compare the feature importance techniques, and we demonstrate the approach on reanalysis climate data. In the climate application, we consider a time period that includes the 1991 volcanic eruption of Mount Pinatubo. This event was a significant stratospheric aerosol injection, which acts as a proxy for an anthropogenic stratospheric aerosol injection. We are able to use the proposed approach to characterize relationships between pathway variables associated with this event that agree with relationships previously identified by climate scientists.

Lagrangian Decomposition of the Atlantic Ocean Heat Transport at 26.5°N

AbstractThe Atlantic Meridional Overturning Circulation (AMOC) plays a critical role in the global climate system through the redistribution of heat, freshwater and carbon. At 26.5°N, the meridional heat transport has traditionally been partitioned geometrically into vertical and horizontal circulation cells; however, attributing these components to the AMOC and Subtropical Gyre (STG) flow structures remains widely debated. Using water parcel trajectories evaluated within an eddy‐rich ocean hindcast, we present the first Lagrangian decomposition of the meridional heat transport at 26.5°N. We find that water parcels recirculating within the STG account for 37% (0.36 PW) of the total heat transport across 26.5°N, more than twice that of the classical horizontal gyre component (15%). Our findings indicate that STG heat transport cannot be meaningfully distinguished from that of the basin‐scale overturning since water parcels cooled within the gyre subsequently feed the northward, subsurface limb of the AMOC.

Changes in Snow Cover and Its Surface Temperature across the Tibetan Plateau Region from 2000 to 2020

Currently, the global climate system is complex and ever-changing, with multiple factors influencing climate change. The Qinghai–Tibet Plateau, known as the “Third Pole” of the Earth, is particularly sensitive to global climate change. Without timely and scientific research on the ecological environment of the Qinghai–Tibet Plateau and without summarizing relevant adaptive strategies, global climate change will impact the sustainable development of the plateau. This study utilized Landsat remote sensing images from 2000 to 2020 to extract the snow cover area and snow temperature of the Qinghai–Tibet Plateau using the snow frequency threshold method. The study analyzed the spatiotemporal characteristics of snow cover and temperature over the 20-year period and investigated some of the climate and topographical driving factors influencing their changes. The results showed that from 2000 to 2020, the permanent snow cover area in the Qinghai–Tibet Plateau region showed a fluctuating decreasing trend, reducing from approximately 12.34 thousand km2 to around 9.01 thousand km2; the permanent snow temperature showed an initial increase followed by a decrease during the same period. The highest annual average snow temperature was approximately −3.478 °C, while the lowest annual average temperature was around −8.150 °C. Over the 20-year period, the snow cover area in the plateau was negatively correlated with temperature and precipitation, while snow temperature was positively correlated with temperature and precipitation. The snow cover in the weak wind areas of the plateau showed a significant reduction. Areas with higher average wind speeds, such as shaded slopes and semi-shaded slopes, had larger snow cover areas. These research findings provide important insights into the protection and management of the ecological environment of the Qinghai–Tibet Plateau.

Spatiotemporal methods for estimating subsurface ocean thermal response to tropical cyclones

Abstract. Tropical cyclones (TCs), driven by heat exchange between the air and sea, pose a substantial risk to many communities around the world. Accurate characterization of the subsurface ocean thermal response to TC passage is crucial for accurate TC intensity forecasts and an understanding of the role that TCs play in the global climate system. However, that characterization is complicated by the high-noise ocean environment, correlations inherent in spatiotemporal data, relative scarcity of in situ observations, and the entanglement of the TC-induced signal with seasonal signals. We present a general methodological framework that addresses these difficulties, integrating existing techniques in seasonal mean field estimation, Gaussian process modeling, and nonparametric regression into an ANOVA decomposition model. Importantly, we improve upon past work by properly handling seasonality, providing rigorous uncertainty quantification, and treating time as a continuous variable, rather than producing estimates that are binned in time. This ANOVA model is estimated using in situ subsurface temperature profiles from the Argo fleet of autonomous floats through a multistep procedure, which (1) characterizes the upper-ocean seasonal shift during the TC season, (2) models the variability in the temperature observations, and (3) fits a thin-plate spline using the variability estimates to account for heteroskedasticity and correlation between the observations. This spline fit reveals the ocean thermal response to the TC passage. Through this framework, we obtain new scientific insights into the interaction between TCs and the ocean on a global scale, including a three-dimensional characterization of the near-surface and subsurface cooling along the TC storm track and the mixing-induced subsurface warming on the track's right side.

Observational Limitations to the Emergence of Climate Signals

AbstractUsing model projections to study the emergence of observable climate signals presumes omniscient knowledge about the climate system. In reality, observational knowledge suffers from data quality and availability issues, for instance data gaps, changes in instrumentation, issues due to gridding and retrieval algorithms. Overlooking such deficiencies leads to misrepresentations of the time of emergence (ToE). We introduce a new definition of ToE that accounts for observational limitations, and show that significant corrections to the ToE may be necessary to achieve the same statistical confidence as would be afforded by omniscient knowledge. We also show how our method can inform future observational needs and observing systems design.

Influence of Terrestrial Nitrogen Dynamics on Mesoscale Near‐Surface Meteorological Fields

AbstractThe influence of biogeochemical cycles, particularly the nitrogen cycle, on near‐surface meteorological fields is a critical yet understudied aspect of regional climate modeling. Neglecting such interactions may compromise the accurate representation of vegetation growth and hydrological processes in climate models, consequently affecting the simulated regional near‐surface climate conditions. In order to quantify such effects, we coupled the nitrogen‐augmented Noah‐MP land surface model with the Weather Research and Forecasting (WRF) model v4.1.2 (hereafter WRF‐CN) for regional climate modeling. Compared to the default WRF simulation without nitrogen dynamics, the WRF‐CN simulated net primary productivity, gross primary productivity (GPP), and leaf area index (LAI) were all higher in the study region. Because WRF underestimated the observed GPP and LAI due to the fixed nitrogen limitation of plant growth, these higher estimations improved WRF‐CN's performance in modeling GPP and LAI, which translated into improved simulations of near‐surface climate. Specifically, for the 2‐m air temperature, compared to WRF, WRF‐CN reduced the mean absolute error and root mean square error by 14.45% and 14.19%, respectively, while increased the Nash‐Sutcliffe efficiency coefficient by 7.23%, with the most pronounced improvements in the regions dominated by croplands. Our findings shed light on the crucial interactions between biogeochemical processes and near‐surface meteorological conditions, emphasizing the significance of incorporating terrestrial nitrogen dynamics in regional climate models. These insights contribute to advancing our understanding of climate system dynamics and improving the accuracy of climate predictions at the mesoscale.

Continued warming of deep waters in the Fram Strait

Abstract. The Fram Strait is the only deep gateway between the Arctic and the rest of the World Ocean, and it is thus a key region to understand how the deep Arctic will evolve. However, studies and data regarding the deep ocean are scarce, making it difficult to understand its role in the climate system. Here, we analyse oceanographic data obtained close to the Fram Strait sill depth of 2500 m by two long-term mooring locations (F11 and HG-FEVI) in the Fram Strait between 2010–2023 to investigate long-term changes in the hydrographic properties. For additional context, we compile hydrographic profile data from the 1980s for the adjacent basins: the Greenland Sea and the Eurasian Basin. At mooring F11 in the western Fram Strait, we find a clear seasonality, with increased Greenland Sea Deep Water (GSDW) presence during summer and increased Eurasian Basin Deep Water (EBDW) presence during winter. Evaluating long-term changes, we find a modest temperature increase of ∼ 0.1 °C for EBDW from the 1980s. For GSDW, south of the Fram Strait, we find a strong temperature increase of ∼ 0.4–0.5 °C for the same period. The different warming rates have led to GSDW becoming warmer than EBDW since ∼ 2017–2018. This means that the Greenland Sea is no longer a heat sink for the Arctic Ocean at depth but is rather a heat source. It is therefore possible that EBDW temperatures will increase faster in the future.

Sea Ice Detection from GNSS-R Data Based on Local Linear Embedding

Sea ice plays a critical role in the Earth’s climate system, and its variations affect ecosystem stability. This study introduces a novel method for detecting sea ice in the Arctic Ocean using bidirectional radar reflections from the Global Navigation Satellite System (GNSS). Utilizing delay-Doppler maps (DDM) from the UK TechDemoSat-1 (TDS-1) satellite mission and surface data from the U.S. National Oceanic and Atmospheric Administration (NOAA), we employ the local linear embedding (LLE) algorithm for feature extraction. This approach notably reduces training costs and enhances real-time performance, while maintaining a high accuracy and robust noise immunity level. Focusing on the region above 70° north latitude throughout 2018, we aimed to distinguish between sea ice and seawater. The extracted DDM features via LLE are input into a support vector machine (SVM) for classification. The results indicate that our method achieves an accuracy of over 99% for selected low-noise data and a monthly average accuracy of 92.74% for data containing noise, while the CNN method has a monthly average accuracy of only 77.31% for noisy data. A comparative analysis between the LLE-SVM approach and the convolutional neural network (CNN) method demonstrated the superior anti-interference capabilities of the former. Additionally, the impact of the sea ice melting period on detection accuracy was analyzed.

Martian Dust Storms: Reviews and Perspective for the Tianwen-3 Mars Sample Return Mission

Dust storms play a crucial role in the climate system and the space environment of Mars, significantly impacting human exploration activities on the planet. The Martian dust storms exhibit significant regional, seasonal and interannual variations due to various controlling factors such as large-scale atmospheric circulation, varying solar radiation forcing, and Martial orbital and rotational motions and their coupling to the atmospheric dynamics. This paper aims to review current understandings of Martian dust storms. This paper begins by elucidating the basic properties of dust storms, their driving mechanisms, and their impacts on atmospheric dynamics, atmospheric electric property, space environment, topography, and Mars explorations. The paper then introduces the observation methods on different platforms, including orbiters and landers/rovers, along with datasets constructed based on these historical observations of Martian dust storms. Finally, we propose dust storm monitoring and predicting for the upcoming Chinese Tianwen-3 Mars sample return mission. It concludes by depicting the future research topics aimed at systematically understanding Martian dust storms.

A missing link in the carbon cycle: phytoplankton light absorption under RCP emission scenarios

Abstract. Marine biota and biogeophysical mechanisms, such as phytoplankton light absorption, have attracted increasing attention in recent climate studies. Under global warming, the influence of phytoplankton on the climate system is expected to change. Previous studies analyzed the impact of phytoplankton light absorption under prescribed future atmospheric CO2 concentrations. However, the role of this biogeophysical mechanism under freely evolving atmospheric CO2 concentration and future CO2 emissions remains unknown. To shed light on this research gap, we perform simulations with the EcoGEnIE Earth system model (ESM) and prescribe CO2 emissions out to the year 2500 following the four Extended Concentration Pathway (ECP) scenarios, which for practical purposes we call Representative Concentration Pathway (RCP) scenarios. Under all RCP scenarios, our results indicate that phytoplankton light absorption leads to a shallower remineralization of organic matter and a reduced export efficiency, weakening the biological carbon pump. In contrast, this biogeophysical mechanism increases the surface chlorophyll by ∼ 2 %, the sea surface temperature (SST) by 0.2 to 0.6 °C, the atmospheric CO2 concentrations by 8 %–20 % and the atmospheric temperature by 0.3 to 0.9 °C. Under the RCP2.6, RCP4.5 and RCP6.0 scenarios, the magnitude of changes due to phytoplankton light absorption is similar. However, under the RCP8.5 scenario, the changes in the climate system are less pronounced due to decreasing ecosystem productivity as temperature increases, highlighting a reduced effect of phytoplankton light absorption under strong warming. Additionally, this work highlights the major role of phytoplankton light absorption on the climate system, suggesting highly uncertain feedbacks on the carbon cycle with uncertainties that may be in the range of those known from the land biota.

Reduced seagrass resilience due to environmental and anthropogenic effects may lead to future die-off events in Florida Bay

Coastal systems often depend on foundation species such as seagrasses that are supported by self-facilitation. Seagrass meadows, however, are threatened worldwide due to climate change and local human impact, disrupting self-facilitation leading to system instability. Florida Bay is a large seagrass dominated coastal ecosystem that suffered from multiple seagrass mortality events over the last half century driven by hypoxia, high water temperatures, hypersalinity, and high biological oxygen demand. These conditions reduce the amount of photosynthetically-derived oxygen in the plant causing sulphide intrusion into meristematic tissues. Using a bay-wide sampling design and long-term monitoring trends of seagrass condition, we investigated the current state of the meadows, sediment characteristics (e.g., organic matter, sulphide, nutrients) and discuss how climate stressors interact with plant and sediment oxygen dynamics. Our survey revealed that at sites where seagrass had been previously denuded by die-off, the dominant seagrass Thalassia testudinum had not recovered, while the pioneering seagrass Halodule wrightii recolonized the impacted areas. Organic matter and sulphide levels were higher at the impacted sites, apparently a persistent characteristic of the formerly dense T. testudinum meadows in central and western Florida Bay. These sediment conditions promote sulphide intrusion of T. testudinum belowground tissue under anoxic conditions. Plant oxidation initially buffers sulphide intrusion, but disruption of this oxidation mechanism due to changing environmental conditions results in widespread mortality and seagrass community collapse. Climate change cannot be fully mitigated by local management, however, attempts can be made to control critical salinity and oxygen levels by increasing freshwater input, reducing hypersalinity and aiming to keep the internal seagrass oxidation feedback intact. Our study shows that the Florida Bay seagrass ecosystem is still recovering four years post die-off and continues to be susceptible to future climate change and system degradation.

Land-Use Feedback under Global Warming—A Transition from Radiative to Hydrological Feedback Regime

Abstract This study examines the effects of land-use (LU) change on regional climate, comparing historical and future scenarios using seven climate models from phase 6 of Coupled Model Intercomparison Project–Land Use Model Intercomparison Project experiments. LU changes are evaluated relative to land-use conditions during the preindustrial climate. Using the Community Earth System Model, version 2–Large Ensemble (CESM2-LE) experiment, we distinguish LU impacts from natural climate variability. We assess LU impact locally by comparing the impacts of climate change in neighboring areas with and without LU changes. Further, we conduct CESM2 experiments with and without LU changes to investigate LU-related climate processes. A multimodel analysis reveals a shift in LU-induced climate impacts, from cooling in the past to warming in the future climate across midlatitude regions. For instance, in North America, LU’s effect on air temperature changes from −0.24° ± 0.18°C historically to 0.62° ± 0.27°C in the future during the boreal summer. The CESM2-LE shows a decrease in LU-driven cooling from −0.92° ± 0.09°C in the past to −0.09° ± 0.09°C in future boreal summers in North America. A hydroclimatic perspective linking LU and climate feedback indicates LU changes causing soil moisture drying in the midlatitude regions. This contrasts with hydrology-only views showing wetter soil conditions due to LU changes. Furthermore, global warming causes widespread drying of soil moisture across various regions. Midlatitude regions shift from a historically wet regime to a water-limited transitional regime in the future climate. This results in reduced evapotranspiration, weakening LU-driven cooling in future climate projections. A strong linear relationship exists between soil moisture and evaporative fraction in midlatitudes. Significance Statement Land–atmosphere feedback involving soil moisture can increase local temperature and affect how land-use (LU) change impacts manifest in a warming climate. Conversely, an increased surface reflectance due to LU change can decrease local temperature in the midlatitude regions. Further, the LU change signal is often mixed with the internal climate variability, making it harder to separate. This study uses a novel technique to separate LU change impact from other climate forcing in the latest generation of climate and Earth system models. In the future climate, soil moisture drying lessens the cooling impact. A large-ensemble climate experiment analysis confirms a significant weakening of the LU-driven cooling impact in the midlatitudes. Both LU and climate changes exacerbate soil moisture drying, leading to a shift toward a water-limited system where hydrological feedback becomes more influential than radiative feedback.

A Nonlinear Full-Field Conceptual Model for ENSO Diversity

Abstract As the strongest year-to-year fluctuation of the global climate system, El Niño–Southern Oscillation (ENSO) exhibits spatial–temporal diversity, which challenges the classical ENSO theories that mainly focus on the canonical eastern Pacific (EP) type. Besides, the complicated interplay between the interannual anomaly fields and the decadally varying mean state is another difficulty in current ENSO theory. To better account for these issues, the nonlinear two-region recharge paradigm model is extended to a three-region full-field conceptual model to capture the physics in the western Pacific (WP), central Pacific (CP), and EP regions. The results show that the extended conceptual model displays a rich dynamical behavior as parameters setting the efficiencies of upwelling and zonal advection are varied. The model can not only generate El Niño bursting behavior but also simulate the statistical asymmetries between the two types of El Niños and the warm and cold phases of ENSO. Finally, since both the anomaly fields and mean states are simulated by the model, it provides a simple tool to investigate their interactions. The strengthening of the upwelling efficiency, which can be seen as an analogy to a cooling thermocline associated with the oceanic tunnel to the midlatitudes, will increase the zonal gradient of the mean state temperature between the WP and EP, i.e., resembling a negative Pacific decadal oscillation (PDO) pattern along the equatorial Pacific. The influence of the zonal advection efficiency is quite the opposite, i.e., its strengthening will reduce the zonal gradient of the mean state temperature along the equatorial Pacific.

The Evolution of Meteorological Satellite Cloud-Detection Methodologies for Atmospheric Parameter Retrievals

The accurate detection of clouds is an important first step in the processing of remotely sensed satellite data analyses and subsequent cloud model predictions. While initial cloud retrieval technology began with the exploitation of one or two bands of satellite imagery, it has accelerated rapidly in recent years as sensor and retrieval technology, creating a new era in space observation exploration. Additionally, the initial emphasis in satellite retrieval technology focused on cloud detection for cloud forecast models, but more recently, cloud screening in satellite-acquired data is playing an increasingly critical role in the investigation of cloud-free data for the retrieval of soil moisture, vegetation cover, ocean color concentration and sea surface temperatures, as well as the environmental monitoring of a host of products, e.g., atmospheric aerosol data, to study the Earth’s atmospheric and climatic systems. With about 60% of the Earth covered by clouds, on average, it is necessary to accurately detect clouds in remote sensing data to screen cloud contaminate data in remote sensing analyses. In this review, the evolution of cloud-detection methodologies is highlighted with advancement in sensor hardware technology and machine learning algorithmic advances. The review takes into consideration the meteorological sensors usually used for atmospheric parameters estimation (thermodynamic profiles, aerosols, cloud microphysical parameters). Moreover, a discussion is presented on methods for obtaining the cloud-truth data needed to determine the accuracy of these cloud-detection approaches.

Pan-Arctic methanesulfonic acid aerosol: source regions, atmospheric drivers, and future projections

Natural aerosols are an important, yet understudied, part of the Arctic climate system. Natural marine biogenic aerosol components (e.g., methanesulfonic acid, MSA) are becoming increasingly important due to changing environmental conditions. In this study, we combine in situ aerosol observations with atmospheric transport modeling and meteorological reanalysis data in a data-driven framework with the aim to (1) identify the seasonal cycles and source regions of MSA, (2) elucidate the relationships between MSA and atmospheric variables, and (3) project the response of MSA based on trends extrapolated from reanalysis variables and determine which variables are contributing to these projected changes. We have identified the main source areas of MSA to be the Atlantic and Pacific sectors of the Arctic. Using gradient-boosted trees, we were able to explain 84% of the variance and find that the most important variables for MSA are indirectly related to either the gas- or aqueous-phase oxidation of dimethyl sulfide (DMS): shortwave and longwave downwelling radiation, temperature, and low cloud cover. We project MSA to undergo a seasonal shift, with non-monotonic decreases in April/May and increases in June-September, over the next 50 years. Different variables in different months are driving these changes, highlighting the complexity of influences on this natural aerosol component. Although the response of MSA due to changing oceanic variables (sea surface temperature, DMS emissions, and sea ice) and precipitation remains to be seen, here we are able to show that MSA will likely undergo a seasonal shift solely due to changes in atmospheric variables.

Zonal Contrasts of the Tropical Pacific Climate Predicted by a Global Constraint

AbstractThe zonal gradients in sea surface temperature and convective heating across the tropical Pacific play a pivotal role in setting the weather and climate patterns globally. Under global warming, the current generation of climate models predict that the zonal gradients will decrease, but the trajectory of the observed trends is the opposite. Theories supporting either of the two projections exist, but there are many relevant processes whose net effect is unclear. In this study, a global constraint – the maximum material entropy production (maxMEP) hypothesis—is considered to help close the gap. The climate system considered here is comprised of a one-layer atmosphere and surface in six regions that represent the western tropical Pacific, eastern tropical Pacific, northern and southern midlatitudes, and northern and southern polar regions. The model conserves energy but does not explicitly include dynamics. The model input is observation-based radiative parameters. The radiative effect of greenhouse gas (GHG) loading is mimicked by prescribing increases in the longwave absorptivity $$\epsilon$$ ϵ . The model solutions predict that zonal contrasts in surface temperature, convective heat flux, and surface pressure increase with increasing $$\epsilon$$ ϵ . While maxMEP solutions in general cannot provide a definite answer to the problem, these model results strengthen the possibility that the trajectory of the observed trend reflects the response to increasing GHG loading in the atmosphere.

Multiple serial correlations in global air temperature anomaly time series.

Serial correlations within temperature time series serve as indicators of the temporal consistency of climate events. This study delves into the serial correlations embedded in global surface air temperature (SAT) data. Initially, we preprocess the SAT time series to eradicate seasonal patterns and linear trends, resulting in the SAT anomaly time series, which encapsulates the inherent variability of Earth's climate system. Employing diverse statistical techniques, we identify three distinct types of serial correlations: short-term, long-term, and nonlinear. To identify short-term correlations, we utilize the first-order autoregressive model, AR(1), revealing a global pattern that can be partially attributed to atmospheric Rossby waves in extratropical regions and the Eastern Pacific warm pool. For long-term correlations, we adopt the standard detrended fluctuation analysis, finding that the global pattern aligns with long-term climate variability, such as the El Niño-Southern Oscillation (ENSO) over the Eastern Pacific. Furthermore, we apply the horizontal visibility graph (HVG) algorithm to transform the SAT anomaly time series into complex networks. The topological parameters of these networks aptly capture the long-term correlations present in the data. Additionally, we introduce a novel topological parameter, Δσ, to detect nonlinear correlations. The statistical significance of this parameter is rigorously tested using the Monte Carlo method, simulating fractional Brownian motion and fractional Gaussian noise processes with a predefined DFA exponent to estimate confidence intervals. In conclusion, serial correlations are universal in global SAT time series and the presence of these serial correlations should be considered carefully in climate sciences.