Temperature Variability Research Articles

Daily temperature variability and mental health-related hospital visits in New York State

BackgroundDespite plausible behavioral and physiological pathways, limited evidence exists on how daily temperature variability is associated with acute mental health-related episodes. ObjectivesWe aimed to explore associations between daily temperature range (DTR) and mental health-related hospital visits using data of all hospital records in New York State during 1995–2014. We further examined factors that may modify these associations, including age, sex, hospital visit type and season. MethodsUsing a case-crossover design with distributed lag non-linear DTR terms (0–6 days), we estimated associations between ZIP Code-level DTR and hospital visits for mood (4.6 million hospital visits), anxiety (2.4 million), adjustment (∼368,000), and schizophrenia disorders (∼211,000), controlling for daily mean temperature, via conditional logistic regression models. We assessed potential heterogeneity by age, sex, hospital visit type (in-patient vs. out-patient), and season (summer, winter, and transition seasons). ResultsFor all included outcomes, we observed positive associations from period minimum DTR (0.1 °C) until 25th percentile (5.2 °C) and between mean DTR (7.7 °C) and 90th percentile (12.2 °C), beyond which we observed negative associations. For mood disorders, an increase in DTR from 0.1 °C to 12.2 °C was associated with a cumulative 16.0% increase (95% confidence interval [CI]: 12.8, 19.2%) in hospital visit rates. This increase was highest during transition seasons (32.5%; 95%CI: 26.4, 39.0%) compared with summer (10.7%; 95%CI: 4.8, 16.8%) and winter (−1.6%; 95%CI: −7.6, 4.7%). For adjustment and schizophrenia disorders, the strongest associations were seen among the youngest group (0–24 years) with almost no association in the oldest group (65+ years). We observed no evidence for modification by sex and hospital visit type. DiscussionDaily temperature variability was positively associated with mental health-related hospital visits within specific DTR ranges in New York State, after controlling for daily mean temperature. Given uncertainty on how climate change modifies temperature variability, additional research is crucial to comprehend the implications of these findings, particularly under different scenarios of future temperature variability.

An improved and extended parameterization of the CO2 15 µm cooling in the middle and upper atmosphere (CO2_cool_fort-1.0)

Abstract. The radiative infrared cooling of CO2 in the middle atmosphere, where it emits under non-local thermodynamic equilibrium (non-LTE) conditions, is a crucial contribution to the energy balance of this region and hence to establishing its thermal structure. The non-LTE computation is too CPU time-consuming to be fully incorporated into climate models, and hence it is parameterized. The most used parameterization of the CO2 15 µm cooling for Earth's middle and upper atmosphere was developed by Fomichev et al. (1998). The valid range of this parameterization with respect to CO2 volume mixing ratios (VMRs) is, however, exceeded by the CO2 of several scenarios considered in the Coupled Climate Model Intercomparison Projects, in particular the abrupt-4×CO2 experiment. Therefore, an extension, as well as an update, of that parameterization is both needed and timely. In this work, we present an update of that parameterization that now covers CO2 volume mixing ratios in the lower atmosphere from ∼0.5 to over 10 times the CO2 pre-industrial value of 284 ppmv (i.e. 150 to 3000 ppmv). Furthermore, it is improved by using a more contemporary CO2 line list and the collisional rates that affect the CO2 cooling rates. Overall, its accuracy is improved when tested for the reference temperature profiles as well as for measured temperature fields covering all expected conditions (latitude and season) of the middle atmosphere. The errors obtained for the reference temperature profiles are below 0.5 K d−1 for the present-day and lower CO2 VMRs. Those errors increase to ∼1–2K d−1 at altitudes between 110 and 120 km for CO2 concentrations of 2 to 3 times the pre-industrial values. For very high CO2 concentrations (4 to 10 times the pre-industrial abundances), those errors are below ∼1 K d−1 for most regions and conditions, except at 107–135 km, where the parameterization overestimates them by ∼1.2 %. These errors are comparable to the deviation of the non-LTE cooling rates with respect to LTE at about 70 km and below, but they are negligible (several times smaller) above that altitude. When applied to a large dataset of global (pole to pole and four seasons) temperature profiles measured by MIPAS (Michelson Interferometer for Passive Atmospheric Spectroscopy) (middle- and upper-atmosphere mode), the errors of the parameterization for the mean cooling rate (bias) are generally below 0.5 K d−1, except between 5×10-3 and 3×10-4 hPa (∼85–98 km), where they can reach biases of 1–2 K d−1. For single-temperature profiles, the cooling rate error (estimated by the root mean square – rms – of a statistically significant sample) is about 1–2 K d−1 below 5×10-3 hPa (∼85 km) and above 2×10-4 hPa (∼102 km). In the intermediate region, however, it is between 2 and 7 K d−1. For elevated stratopause events, the parameterization underestimates the mean cooling rates by 3–7 K d−1 (∼10 %) at altitudes of 85–95 km and the individual cooling rates show a significant rms (5–15 K d−1). Further, we have also tested the parameterization for the temperature obtained by a high-resolution version of the Whole Atmosphere Community Climate Model (WACCM-X), which shows a large temperature variability and wave structure in the middle atmosphere. In this case, the mean (bias) error of the parameterization is very small, smaller than 0.5 K d−1 for most atmospheric layers, reaching only maximum values of 2 K d−1 near 5×10-4 hPa (∼ 96 km). The rms has values of 1–2 K d−1 (∼20 %) below ∼2×10-2 hPa (∼80 km) and values smaller than 4 K d−1 (∼2 %) above 10−4 hPa (∼105 km). In the intermediate region between ∼5×10-3 and ∼2×10-4 hPa (85–102 km), the rms is in the range of 5–12 K d−1. While these values are significant in percentage at ∼5×10-3–5×10-4 hPa, they are very small above ∼5×10-4 hPa (96 km). The routine is very fast, taking (1.5–7.5) ×10-5 s, depending on the extension of the atmospheric profile, the processor and the Fortran compiler.

Generative design of conformal cooling channels for hybrid-manufactured injection moulding tools

Effective cooling systems for injection moulding (IM) tools are critical to reducing manufacturing costs and cycle time for the polymer parts produced. This work presents a novel automated methodology for designing conformal cooling channels (CCCs) for IM tools via interlinking commercial moulding simulation tools with custom scripts. These scripts adjust CCC design in response to the spatial variability in global and local temperatures at the mould tool-part interface (MTPI) via generative design (GD). Four tool designs for manufacturing a simplified part were analysed numerically simulation and experimentally, including tools with either straight-drilled (non-conformal) cooling channels (tool steel), a manually designed CCC system (stainless steel or bronze alloy), or an automatically designed CCC system (stainless steel). While both manually designed CCC tools cool the part faster than the non-conformal tool (3–5% predicted vs. 40% measured for stainless steel and 9–12% predicted vs. 40% measured for bronze alloy), the GD-optimised CCC tool outperformed both (15–30% predicted faster cooling vs. 70% measured). The predicted MTPI temperature feature achieves reductions to both maximum and spatial variability in MTPI temperatures, which lead directly to significant reductions in manufacturing cycle time and polymer part warpage. These findings could have a major impact on the IM industry by reducing tool design costs and raw material waste via improving moulded part quality.

Temporal variability in water content, temperature, and electrical conductivity of sandy soil: Responses to soil-improving cropping systems (SICS)

There is little information on the effect of soil-improving cropping systems (SICS) on the temporal variability of soil volumetric water content (WC), temperature (T), soil temperature amplitude (A_T), and electrical conductivity (EC) in low-quality sandy soils although they are increasingly being used in agriculture. The objective of this study was to determine the effects of the following SICS: liming (L), leguminous catch crops for green manure (LU), farmyard manure (M), and liming + leguminous catch crops for green manure + farmyard manure together (LLUM), and the control (C) on temporal changes in the three properties simultaneously in a field experiment on sandy soil established in 2016 in Poland. The monitoring was performed using TEROS 12 sensors in the soil layers of 5–10, 20–25, and 30–35 cm from 25 April 2019–30 April 2020 with spring wheat as the main crop. The results showed that the average WC over the entire investigation period in the 5–10 cm layer was 0.206 m3 m–3 in the control and increased in the L, LU, M, and LLUM variants to 0.245, 0.219, 0.241, 0.262 m3 m–3, respectively. The highest WC was recorded in the LLUM system during most of the investigation period. The average T in the 5–10 cm layer increased in L, LU, and M by 0.221–0.262 °C and in LLUM by 0.052 °C. The average monthly soil temperature amplitudes in the soil layer 5–10 cm tended to be higher under all SICS vs. C except LLUM vs. C. The average EC in the 5–10 cm layer in variant C (0.066 mS cm–1) increased in the L, LU, M, and LLUM systems to 0.067, 0.090, 0.151, and 0.107 mS cm–1, respectively. The shapes of the temporal variability of WC and EC were similar in all SICSs, while the increase in the response to the combined effects of the SICSs and precipitation during the growing season was greater for the EC. The temporal variability of all soil properties in response to the SICSs and weather course and their averages declined with depth. The amplitudes of soil T in all treatments were lowest in the 30–35 cm layer. This study provides helpful information for the selection of the most suitable SICS in terms of soil properties influencing productivity and other soil functions.

Energy and exergy analysis of an experimental NH3-LiNO3 air-conditioning absorption system

An energy and exergy analysis of an experimental air-conditioning absorption system operating with the ammonia-lithium nitrate mixture has been performed. The system was operated under controlled and steady-state conditions. The main operating parameters, such as flow ratio, coefficient of performance, exergy coefficient of performance, and exergy destruction in each one of the main components, were determined at different heat sources and condensing temperatures. The coefficients of performance varied between 0.5 and 0.65, while the exergy coefficients of performance varied between 0.1 and 0.2. Clear trends were found for the coefficient of performance and flow ratio as a function of the generator and condenser temperatures; however, no trends were found in the exergy coefficients of performance due to the high variability of the ambient temperature. The highest values of the exergy destruction occurred in the generator, followed by the condenser and the evaporator, while the lowest values were obtained in the absorber and the economizer.

Numerical Modeling of Atmospheric Temperature and Stratospheric Ozone Sensitivity to Sea Surface Temperature Variability

The results of numerical experiments with a chemistry–climate model of the lower and middle atmosphere are presented to study the sensitivity of the polar stratosphere of the Northern and Southern Hemispheres to sea surface temperature (SST) variability, both as a result of interannual variability associated with the Southern Oscillation, and because of long-term increases in SST under global warming. An analysis of the results of model experiments showed that for both scenarios of SST changes, the response of the polar stratosphere for the Northern and Southern Hemispheres is very different. In the Arctic, during the El Niño phase, conditions are created for the polar vortex to become less stable, and in the Antarctic, on the contrary, for it to become more stable, which is expressed in a weakening of the zonal wind in the winter in the Arctic and its increase in the Antarctic, followed by a spring decrease in temperature and concentration of ozone in the Antarctic and their increase in the Arctic. Global warming creates a tendency for the polar vortex to weaken in winter in the Arctic and strengthen it in the Antarctic. As a result, in the Antarctic, the concentration of ozone in the polar stratosphere decreases both in winter (June–August) and, especially, in spring (September–November). Global warming may hinder ozone recovery which is expected as a result of the reduced emissions of ozone-depleting substances. The model results demonstrate the dominant influence of Brewer–Dobson circulation variability on temperature and ozone in the polar stratosphere compared with changes in wave activity, both with changes in SST in the Southern Oscillation and with increases in SST due to global warming.

Influence of window and door opening on office room environment and human thermal sensation during different seasons in moderate climate

Natural ventilation is commonly used in buildings because it does not require energy, and its various strategies, for example ventilation through an open window, can improve the indoor environment quality, including the thermal comfort of users. This study aimed to determine accurate environmental parameters typical for naturally ventilated rooms for three seasons: summer, autumn and winter, and to assess the occupants' thermal sensation in the mentioned conditions. Based on the measurements, the spatial distribution and time variability of air temperature and air speed in an office room with a stack natural ventilation system were assessed. The resulting data on the indoor environment parameters at different seasons and with various door and window opening configurations were used as an input to simulate human thermal sensation. Three models were used for simulation: predicted mean vote (PMV), the thermal sensation (TS) model developed by Zhang et al., and the advanced dynamic thermal sensation (DTS) model by Fiala et al., which allowed the demonstration of differences in the thermal comfort assessment depending on the method. Measurements have shown that opening the window with the door closed has a negligible impact on the parameters indoor environment parameters in summer; opening the door (cross-ventilation) changes this situation. The effect increased during autumn and winter, which affects users' thermal sensations. The assessment based on the most popular PMV model differed from the indications of advanced DTS and TS models showing significant discrepancies, which may lead to a misinterpretation of thermal comfort for individuals in the room.

Trends in intraseasonal temperature variability in Europe: Comparison of station data with gridded data and reanalyses

AbstractTrends in temperature variability are often referred to have higher effect on temperature extremes than trends in the mean. We investigate trends in three complementary measures of intraseasonal temperature variability: (a) standard deviation of mean daily temperature (SD), (b) mean absolute value of day‐to‐day temperature change (DTD) and (c) 1‐day lagged temporal autocorrelation of temperature (LAG). It is a well‐established fact that different types of data (station, gridded, reanalyses) provide different temperature characteristics and particularly their trends. Moreover, we have uncovered that trends in measures of variability are considerably sensitive to data inhomogeneities. Therefore, we use five different datasets, one station based (ECA&D), one gridded (EOBS) and three reanalyses (JRA‐55, NCEP/NCAR, 20CR), and compare them. The period from 1961 to 2014 where all datasets overlap is examined, and the linear regression method is utilized to calculate trends of investigated measures in summer and winter. Intraseasonal temperature variability tends to decrease in winter, especially in eastern and northern Europe, where trends below −7%·decade−1 are detected for all measures. Decreases in DTD and LAG (indicating increase in persistence) prevail also in summer while summer SD tends to increase. The increase in the width of temperature distribution and the simultaneous increase in persistence indicate a tendency towards the rise in the frequency of extended extreme events in summer. Unlike previous studies, our results imply that reanalyses are not the least accurate in determining trends. JRA‐55 appears to be the least diverging from other datasets, while the largest discrepancies are detected for DTD at station data.

Probabilistic medium-range forecasts of extreme heat events over East Asia based on a global ensemble forecasting system

This study suggests a methodology for probabilistic forecasts of the extreme heat events in East Asia based on an operational global ensemble prediction used by the Korea Meteorological Administration (KMA). It focuses on the medium range of up to 11 days, providing probabilities of heatwave and tropical night occurrence each day. Forecast validation in the summer from 2016 to 2021 shows that the deterministic heatwave forecast provides 5 days of optimal forecast range, while the probabilistic forecast can extend the practically predictable range up to 10 days in the Korean Peninsula and 7 days in Japan, respectively. Comparing prediction skills for heatwave and tropical night, the skills for tropical night tend to be inferior, presumably due to complex mechanisms of the tropical night and large uncertainty in the numerical model, such as microphysics and radiation. In addition, the coarse resolution of the operational system does not seem to resolve temperature variability at night. As a case study, this study also examines the forecast of the onset and offset of the 2018 South Korean heatwave event. The temporal evolution of the heatwave matches well with the changes in the upper-level atmospheric circulation pattern, which can be used for useful forecast guidance. This probabilistic forecast based on the global ensemble forecasting system is expected to provide reliable prediction information for heatwaves in advance, reducing exposure to extreme events.

Modelling air, ground surface and permafrost temperature variability across four dissimilar valleys, Yukon, Canada

Spatial maps of the air and ground thermal regime were generated for four Yukon valleys. The aim was to model air, ground surface and ground temperature (at fine spatial resolution) using locally measured inverted surface lapse rates (SLR), to better predict temperature along an elevation gradient. These local models were then compared to a regional permafrost probability model, which utilized differing inversion assumptions, as well as circumpolar and national models generated without considering inversions. Overall, permafrost probability in the regional model matched well with the local models where assumptions of treeline and inverted SLRs held true. When normal SLRs were assumed, permafrost presence was overestimated in each valley. This discrepancy was greatest at high elevations where permafrost was predicted to be coldest and most widespread. However, the difference between valleys was dependent on surface and subsurface characteristics such as higher snow cover, mature forest or thick organic layers showed a greater disassociation from the air temperature overall. Appropriate characterization of the SLR is essential for accurate predictions of the ground thermal regime’s spatial distribution and permafrost presence. These models also provide a starting point for better predictions of warming in these valleys and other areas subject to inversions of similar magnitudes.

An intelligent climate monitoring system for hygrothermal virtual measurement in closed buildings using Internet-of-things and artificial hydrocarbon networks

Studies analyzing indoor thermal environments comprising temperature and humidity may be insufficient when obtaining data from sensors, which may be susceptible to inaccurate or failed information from internal and external factors. Therefore, this study proposes an intelligent climate monitoring using a supervised learning method for virtual hygrothermal measurement in enclosed buildings used to predict temperature and relative humidity when a sensor failure is detected. The methodology comprises the data collection from a wireless sensor network, the building of the learning model for predicting the dynamics of environmental variables, and the implementation of a sensor failure detection model. We use an artificial hydrocarbon network as the learning model for their simplicity and effectiveness under uncertain and noisy data. The experiments use data acquired in two settings: (1) a laboratory office and (2) a museum storage room. The first scenario has multiple workstations, and the staff turns on or off the air conditioning depending on the feeling of comfort, generating an uncontrolled environment for the variables of interest. The second scenario has controlled temperature and humidity to ensure the conservation conditions of the museum pieces. Both scenarios used 12 sensors that acquired data for one month, providing an average of 58,300 values for each variable. Results of the proposed methodology provide 95% of accuracy in terms of sensor failure detection and identification, and less than 0.22% of tolerance variability in temperature and humidity after sensor accommodation in both scenarios.

Logistic curve modelling of sea surface temperature and latent heat flux variability in the Tropical Indian Ocean

The Tropical Indian Ocean (TIO) is one of the most vulnerable regions to climate change due to its unique ocean-atmosphere interactions. The region is characterized by warm temperatures, high evaporation rates, and strong convection, making it particularly vulnerable to greenhouse gases. This susceptibility leads to an overall trend of warming across the Earth's surface and atmosphere, intensifying sea surface temperatures (SST) and increasing evaporation rates, consequently influencing Surface Latent Heat Flux (SLHF). The strong influence of both SST and SLHF on atmospheric circulation and precipitation patterns makes them critical factors in determining the region's climate, particularly in monsoon-dominated regions. Analysis reveals a consistent upward trend in both SST and SLHF in the central region of TIO. To model this, standard Logistic Curve Model (LCM) has been applied to these parameters averaged over the central region of the TIO. The model is run for a period of 20 years from 2001 to 2020, grouped into four lustrums. The LCM-derived SST and SLHF are in good consistent with observed datasets during the above periods with, correlation coefficients ranging from 0.92 to 0.96 for SST and 0.86–0.90 for SLHF. Extending the model spatially across the entire TIO region explains the ability to project fluctuations in SST and SLHF values across different seasons. These findings highlight the model's relevance for capturing short-term, long-term, and seasonal variability of the parameters, providing important insights into regional climate dynamics.

The trends and spatiotemporal variability of temperature and rainfall in Hulbarag district, Silte Zone, Ethiopia

Ethiopia gets its agricultural water primarily from rainfall. This study was intended to investigate current climate variability and trends across space and time. Daily gridded temperature and rainfall data from 1993 to 2022 in the Hulbarag district, Silte Zone of Ethiopia was obtained from the Ethiopian National Metrological Institute and the Climate Hazard Group Infrared Precipitation with Station. The trends and variability of temperature and rainfall were analyzed using the Mann-Kendall trend test, Sen's slope, coefficient of variation, precipitation concentration index, and rainfall anomaly index. The results indicated that annual, spring, and summer rainfall revealed statistically significant decreasing trends at Hulbarag and Sankura stations, with the magnitude of −13.4, −11.6, and −10.6 mm per year and −6.8, −3.6 and −10.9 mm per year respectively. Conversely, autumn and winter season rainfall showed statistically significant increasing trends at Hulbarag and Sankura stations, with the magnitude of 5.1 and 5.5mm per year and 3.4 and 1.84mm per year consecutively. Between 43 % and 47 % of the observation periods had negative anomalies. The average yearly temperature, average yearly minimum temperature, and average yearly maximum temperatures at Sankura and Fonko stations all displayed statistically significant increasing trends, with a magnitude of 0.091 °C, 0.009 °C and 0.051 °C per year and 0.03 °C,0.01 °C and 0.0022 °C per year successively. It is advisable to develop a farming system that is climate-resilient by improving the adaptive capacity of wheat and maize-growing farmers by expanding the availability of early maturing seeds, changing crop calendars, and enhancing proactive and credible climate information services.

Mitigating dengue incidence through advanced Aedes larval surveillance and control: A successful experience from Pakistan.

Dengue fever is a viral disease caused by one of four dengue stereotypes (Flavivirus: Flaviviridae) that are primarily transmitted by Aedes albopictus (Skuse) and Aedes aegypti (L.). To safeguard public health, it is crucial to conduct surveys that examine the factors favouring the presence of these species. Our study surveyed 42 councils across four towns within the Bhakkar district of Punjab Province, by inspecting man-made or natural habitats containing standing water. First, door-to-door surveillance teams from the district health department were assigned to each council to surveillance Aedes species and dengue cases. Second, data collection through surveillance efforts, and validation procedures were implemented, and the verified data was uploaded onto the Dengue Tracking System by Third Party Validation teams. Third, data were analysed to identify factors influencing dengue fever cases. The findings demonstrated the following: (1) Predominantly, instances were discerned among individuals who had a documented history of having travelled beyond the confines of the province. (2) Containers associated with evaporative air coolers and tyre shops were responsible for approximately 30% of the Aedes developmental sites. (4) Variability in temperature was responsible for approximately 45% of the observed differences in the quantity of recorded Aedes mosquito developmental sites. (5) Implementation of dengue prevention initiatives precipitated a 50% reduction in Aedes-positive containers, alongside a notable 70% decline in reported cases of dengue fever during the period spanning 2019 to 2020, while the majority of reported cases were of external origin. Aedes control measures substantially curtailed mosquito populations and lowered vector-virus interactions. Notably, local dengue transmission was eliminated through advanced and effective Aedes control efforts, emphasising the need for persistent surveillance and eradication of larval habitats in affected regions.

Does Engine Oil Type Affect Fuel Consumption in Passenger Vehicles? A Two-Year Investigation

Reducing fuel consumption to decrease CO2 emissions has become a key development factor in the automotive industry. An effective way to decrease fuel consumption is to reduce the influence of various sources of energy loss. One way to increase engine efficiency is to use low-viscosity engine oils to reduce friction losses in the engine’s tribological systems. The aim of the article was to analyze the relationship between the type of engine oil and fuel consumption in a group of 12 passenger cars. This was a homogenous group of identical cars, equipped with the same engine, operated under very similar conditions. Three groups of engine oils (Revline, Total, Orlen) were tested in vehicles and stressed with comparable workloads. The experiment was conducted over two years (two stages of research). The collected results were presented using graphs and compared using statistical tests, split into two stages of research, with four seasonal temperature groups. The study provides a detailed description of fuel consumption differences taking into account variations in ambient temperature. The analyses were focused on finding answers to two research questions: does the type of engine oil affect the variation in combustion levels, and what is the variability of fuel consumption in different seasons due to the ambient temperature variability? Briefly, in both stages of the study, vehicles using Revline oil attained the highest average fuel consumption throughout the study period. Vehicles using Total oil showed similar results to those using Revline oil, with the difference in fuel consumption not as noticeable during warmer months. Conversely, vehicles using Orlen oil demonstrated the lowest fuel consumption values during colder months, but higher levels during warmer months.

The 2019 Raikoke eruption as a testbed used by the Volcano Response group for rapid assessment of volcanic atmospheric impacts

Abstract. ​​​​​​​The 21 June 2019 Raikoke eruption (48° N, 153° E) generated one of the largest amounts of sulfur emission to the stratosphere since the 1991 Mt. Pinatubo eruption. Satellite measurements indicate a consensus best estimate of 1.5 Tg for the sulfur dioxide (SO2) injected at an altitude of around 14–15 km. The peak Northern Hemisphere (NH) mean 525 nm stratospheric aerosol optical depth (SAOD) increased to 0.025, a factor of 3 higher than background levels. The Volcano Response (VolRes) initiative provided a platform for the community to share information about this eruption which significantly enhanced coordination efforts in the days after the eruption. A multi-platform satellite observation subgroup formed to prepare an initial report to present eruption parameters including SO2 emissions and their vertical distribution for the modeling community. It allowed us to make the first estimate of what would be the peak in SAOD 1 week after the eruption using a simple volcanic aerosol model. In this retrospective analysis, we show that revised volcanic SO2 injection profiles yield a higher peak injection of the SO2 mass. This highlights difficulties in accurately representing the vertical distribution for moderate SO2 explosive eruptions in the lowermost stratosphere due to limited vertical sensitivity of the current satellite sensors (±2 km accuracy) and low horizontal resolution of lidar observations. We also show that the SO2 lifetime initially assumed in the simple aerosol model was overestimated by 66 %, pointing to challenges for simple models to capture how the life cycle of volcanic gases and aerosols depends on the SO2 injection magnitude, latitude, and height. Using a revised injection profile, modeling results indicate a peak NH monthly mean SAOD at 525 nm of 0.024, in excellent agreement with observations, associated with a global monthly mean radiative forcing of −0.17 W m−2 resulting in an annual global mean surface temperature anomaly of −0.028 K. Given the relatively small magnitude of the forcing, it is unlikely that the surface response can be dissociated from surface temperature variability.

Can seasonal prediction models capture the Arctic mid‐latitude teleconnection on monthly time scales?

AbstractThis study explores Arctic warming's effect on Eurasia's temperature variability, notably the warm Arctic–cold Eurasia (WACE) pattern, and assesses seasonal prediction models' accuracy in capturing this phenomenon and its monthly variation. Arctic warming events are categorized into deep Arctic warming (DAW), shallow Arctic warming (SAW), warming aloft (WA), and no Arctic warming (NOAW), based on the temperatures at 2 m and 500 hPa in the Barents–Kara Sea. It is revealed that DAW events are significantly correlated with monthly cold temperature anomalies in East Asia, predominantly occurring in January–February, excluding December. This study evaluates two primary capabilities of seasonal prediction models: their proficiency in forecasting these Arctic warming events, particularly DAW, and their ability to replicate the spatial patterns associated with DAW. Some models demonstrated notable predictive skill for DAW events, with enhanced performance in January and February. Regarding spatial pattern reproduction, models showed limited alignment with the reference dataset over the Northern Hemisphere (above 25° N) in December, whereas a higher degree of concordance was observed in January–February. This indicates their capability in capturing the atmospheric circulation patterns associated with DAW, pointing to areas where model performance can be enhanced.

Longitudinal assessment of extreme climate events in Kinnaur district, Himachal Pradesh, north-western Himalaya, India.

It is vital to keep an eye on changes in climatic extremes because they set the stage for current and potential future climate, which usually have a reasonable adverse impact on ecosystems and society. The present study examines the variability and trends in precipitation and temperature across seasons in the Kinnaur district, offering valuable insights into the complex dynamics ofthe Himalayan climate. Using Climatic Research Unit gridded Time Series (CRU TS) datasets from 1951 to 2021, the study analyzes the data to produce 28 climate indices based on India Meteorological Department (IMD) convention indices and Expert Team on Climate Change Detection and Indices (ETCCDI). Although there may be considerable variation in climate indices in terms of absolute values within different products, there is consensus in both long-term trends and inter-annual variability. Analysis shows that even within a small area, there is variability in the magnitude and direction of historic temperature trends. Initially, the datawere subjected to rigorous quality control procedures, which involved identifying anomalies. Statistical analysis like trend analysis, employing Mann-Kendall test and Sen's slope estimator, reveal significant (p < 0.05) increase in consecutive dry days (CDD) at 0.03days/year and decrease in consecutive wet days (CWD) at 0.02days/year. Notably, the frequency of heavy precipitation occurrences showed an increasing trend. Changes in precipitation in the Western Himalaya are driven by a complex interplay of orographic effects, monsoonal dynamics, atmospheric circulation patterns, climate change, and localized factors such as topography, atmospheric circulation patterns, moisture sources, land-sea temperature contrasts, and anthropogenic influences. Moreover, in case of temperature indices, there is significant increasing trend observed. Temperature indices indicate a significant annual increase in warm nights (TN90p) at 0.06%/year and warm days (TX90p) at 0.11%/year. Extreme temperature events have been trending upward, with monthly daily maximum temperature (TXx) increasing by 1.5°C yearly. This study enhances our comprehension of the global warming phenomenon and underscores the importance of acknowledging alterations in the water cycle and their repercussions on hydrologic resources, agriculture, and livelihoods in the cold desert of the northwestern Indian Himalaya.

Revealing trends and persistent cycles of non-autonomous systems with autonomous operator-theoretic techniques

An important problem in modern applied science is to characterize the behavior of systems with complex internal dynamics subjected to external forcings. Many existing approaches rely on ensembles to generate information from the external forcings, making them unsuitable to study natural systems where only a single realization is observed. A prominent example is climate dynamics, where an objective identification of signals in the observational record attributable to natural variability and climate change is crucial for making climate projections for the coming decades. Here, we show that operator-theoretic techniques previously developed to identify slowly decorrelating observables of autonomous dynamical systems provide a powerful means for identifying nonlinear trends and persistent cycles of non-autonomous systems using data from a single trajectory of the system. We apply our framework to real-world examples from climate dynamics: Variability of sea surface temperature over the industrial era and the mid-Pleistocene transition of Quaternary glaciation cycles.

The temporal variability of Io’s hotspots

We investigate the variability of the power emission of Io’s hotspots by using recent Juno/JIRAM infrared observations. The Jovian Infrared Auroral Mapper (JIRAM) is an imaging spectrometer which began observing Jupiter in August 2016. Although observing Jupiter’s moons is not its primary objective, JIRAM can use the frequent opportunities to observe Io (up to once per orbit) to gather infrared images and spectra of its surface. The present study uses the data acquired by JIRAM during the last 2 years, including the location and morphology of Io’s hotspots, and the temporal variability of the total output. A new photometric model for the hotspots and the dayside surface has been developed, which permits us to disentangle the temporal variability from the changes in the observation geometry. While the latitudinal dependence of the power output is not well constrained, low-latitude hotspots show a significantly more intense temporal variability and greater temperature.