Climate Indices Research Articles

Mitigating urban heat island effects using trees in planters with varied crown shapes

Urban downtown areas are often overheated and contribute to the creation of urban heat island (UHI) phenomenon. Implementation of trees is considered an effective way to mitigate UHI and improve outdoor thermal comfort (OTC) in densely built-up areas.This study aims to investigate the potential of applying additional trees in planters to mitigate UHI effects and improve OTC in open urban areas. Simulation of OTC is performed for hot summer days using Universal Thermal Climate Index (UTCI). In this study, we have devised a method to determine favourable locations for a predetermined number of additional trees, with different crown shapes (cylinder, sphere and cone), to improve OTC conditions. In the proposed method, an evolutionary algorithm based on natural selection was used as it is effective for solving problems that have a large number of combinations. The method considers the geometry of the built environment, the geometry, locations and number of additional trees, weather data and UTCI simulation.The method was applied to three different urban morphologies located in the downtown of Novi Sad, Serbia. The results show that, depending on urban geometries and morphologies, different crown shapes lead to different UTCI reduction. Cylinder-shaped and sphere-shaped tree crowns showed to be the most effective in reducing heat stress. The positioning of additional trees reduced the UTCI values at single manikin locations by up to 6.11 °C, indicating that the process of determining their locations is crucial for mitigating the heat and improving OTC conditions during hot summer days.

Bayesian-Neural-Network-Based Approach for Probabilistic Prediction of Building-Energy Demands

Reliable prediction of building-level energy demand is crucial for the building managers to optimize and regulate energy consumption. Conventional prediction models omit the uncertainties associated with demand over time; hence, they are mostly inaccurate and unreliable. In this study, a Bayesian neural network (BNN)-based probabilistic prediction model is proposed to tackle this challenge. By quantifying the uncertainty, BNNs provide probabilistic predictions that capture the variations in the energy demand. The proposed model is trained and evaluated on a subset of the building operations dataset of Lawrence Berkeley National Laboratory (LBNL), Berkeley, California, which includes diverse attributes related to climate and key building-performance indicators. We have performed thorough hyperparameter tuning and used fixed-horizon validation to evaluate trained models on various test data to assess generalization ability. To validate the results, quantile random forest (QRF) was used as a benchmark. This study compared BNN with LSTM, showing that BNN outperformed LSTM in uncertainty quantification.

Extreme Rainfall Analysis in Pernambuco, Northeast Brazil, Using a High‐Resolution Gridded Dataset

ABSTRACTThis paper presents a detailed spatio‐temporal analysis of the rainfall in the state of Pernambuco, Northeast Brazil. It is based on climate indices for extreme precipitation recommended by the Expert Team on Climate Change Detection, Monitoring and Indices. To accomplish this, daily rainfall 1data (1961–2019) were extracted from 809 high‐resolution grid points (0.1° × 0.1°) using the Brazilian Daily Weather Gridded Data (BR‐DWGD). The significance and magnitude of index trends were assessed using the modified Mann–Kendall and Sen's slope tests. This study also examined whether there existed a significant difference in climate indices among the three regions (Sertão, Agreste and Zona da Mata) within the state. The findings revealed notable significant negative trends in the PRCPTOT, R10mm, R20mm, Rx1day, Rx5day and CWD indices across all regions of Pernambuco, exhibiting a gradient from the coast to the state's interior. Reduction values of up to 15 mm year−1 for PRCPTOT, 0.7 day year−1 for R10mm, 0.2 day year−1 for R20mm, 0.01 mm year−1 for Rx1day, 0.03 mm year−1 for Rx5day, 0.4 day year−1 for CWD were observed. Furthermore, an alarming pattern was also noted for CDD, displaying a higher concentration of significant positive trends in all regions of the state, with estimated increases of up to 1.4 day year−1. Conversely, a balance of trends—both positive and negative—was observed across the entire state for R95p and R99p, with a majority of trends proving non‐significant. SDII exhibited a higher frequency of grid points showing a significant positive trend, particularly notable in the Sertão and Zona da Mata regions, where significant differences in the index values were absent. However, the remaining indices showcased notable regional differences, with values decreasing from the east to the west of the state, except for CDD. This study will assist decision makers, providing detailed long‐term information essential for preventing natural disasters and supporting socioeconomic and environmental policies in the state.

Synthesis of a hydrological, water chemistry, and contaminants research program in the Peace-Athabasca Delta (Canada) to inform long-term monitoring of shallow lakes

In a multistressor world, evidence-based stewardship of aquatic ecosystems requires long-term monitoring data to understand the timing and magnitude of environmental change and potential causes. At the Peace-Athabasca Delta (PAD; northeastern Alberta, Canada), concern for aquatic ecosystem degradation has triggered renewed and urgent calls by Indigenous, national, and international governance bodies for implementation of a long-term lake monitoring program capable of tracking changes in hydrological conditions and contaminant deposition attributable to major energy projects located upstream, climate change, and other unnatural and natural processes. Challenges imposed by the delta's size, hydrological complexity, inaccessibility of lakes, and other factors, however, have long impeded implementation of a delta-wide lake monitoring program. To address this pressing need, here we review and synthesize results obtained during 7 years (2015–2021) of intensive, multifaceted research at 60 shallow lakes spanning the delta's broad hydroecological gradients to inform an integrated hydrology, water chemistry, and contaminants monitoring program. The research involved systematic, repeated measurements of water isotope composition, water depth variation, water chemistry and turbidity, and metal(loid) concentrations in lake surface sediment and periphytic biofilm. Results reveal marked spatial and temporal variation of hydrological processes and their affects on lake water balance and depth, strong association between hydrological processes and lake water chemistry, and that concentrations of nickel and vanadium (key oil sands indicators) remain within the range of natural variation. Correspondence of generalized additive model trendlines for isotope-derived lake evaporation-to-inflow ratios and water chemistry with climate indices (Pacific Decadal Oscillation, Oceanic Niño Index) demonstrates the sensitivity, and predictability, of lake ecosystem processes in the delta to large-scale climatic patterns. We provide recommendations for field sampling, sample analysis, data display, and integration of information for ongoing monitoring at the PAD. These approaches are readily transferable to other complex landscapes with abundant shallow waterbodies threatened by multiple stressors that may alter hydrological regimes and contaminant delivery.

Escalating threat of human-perceived heatwaves in Brazil

Abstract Heatwaves pose significant threats to socioeconomic and environmental systems, with their intensity and frequency expected to increase due to climate change. Despite their critical impacts, future heatwaves in Brazil remain underexplored, especially from a human-perceived perspective, which is crucial for assessing potential public health impacts. Here, we propose a method to assess heatwaves using the humidex (H) – a climate index that combines temperature and relative humidity to indicate human-perceived heat - alongside traditional temperature-based measures. Using bias-corrected simulations from 10 CMIP6 models under SSP2-4.5 and SSP5-8.5 scenarios, we quantified projected changes in heatwaves across Brazil. The results indicate that heatwaves will become more severe and prolonged, with greater changes under the SSP5-8.5 scenario by the end of the century, particularly in the North, Northeast, and Central regions. The magnitude of human-perceived heatwaves is expected to rise faster than temperature-based ones, underscoring the need for public health-focused assessments. CMIP6 models strongly agree on increased future heatwaves, potentially tripling population exposure in most Brazilian states, with the Southeast experiencing greater changes due to its larger population. These events are expected not only to affect more people but also to be more severe, exceeding over 60 days per year of serious danger (H > 45°C) by the end of the century under SSP5-8.5. Record-shattering events in the historical period are projected to become the norm by mid-century, highlighting the accelerating nature of these extreme events. Our findings emphasize the importance of considering human-perceived heat in climate impact studies and public health planning to mitigate potential impacts.

Past Groundwater Drought in the North American Cordillera

AbstractGroundwater level records in North America are relatively short (<60 years), preventing long‐term analysis of historical changes in groundwater levels associated with drought. In this study, tree ring widths are used to reconstruct groundwater levels in three regions in the North American Cordillera: Central British Columbia (BC), Canada, the Southern Interior Region of BC, and the San Luis Valley in Colorado, USA. Periods with severe drought conditions, identified using regime shift and threshold analyses were: 1890–1900 and 1950–1970 in Colorado, around 1920–1940 in the BC Interior, and 1935–1945 in Central BC. The groundwater level reconstructions are correlated with several climate indices and have similar regime shifts as identified in streamflow and drought records. The groundwater level reconstructions are strongly related to winter snowpack, suggesting that the observed trend of declining snowpack in recent years may lead to declining groundwater availability in these regions.

Forest soil CO2 emission in Quercus robur level II monitoring site

Abstract In this study, the soil CO2 emission was analysed at the level II ICP Forests monitoring plot in Serbia in the pedunculate oak forest. Two plots of pedunculate oak (Quercus robur L.) were selected for this study. The main question was to determine the differences in the impact of management (human impact) on CO2 emission. Different time periods were compared to identify the main factors affecting soil CO2 emission. Sampling was done by chambers. During the study period, climate indicators were quite different. A strong positive correlation between the soil temperature and soil CO2 emission, as well as a strong negative correlation between the soil moisture and soil CO2 emission, was found in the spring aspect (Plot). In other cases, a moderate to weak correlation was found. Multiple linear regressions showed that CO2 emission from soil was primarily controlled by soil moisture. Increasing soil water content had a positive effect on soil respiration (except in spring). The effect of soil temperature appeared in the multiple regressions as a secondary factor during the period studied, and an increase in temperature resulted in a decrease in soil respiration (except in spring).

Optimizing Outdoor Thermal Comfort for Educational Buildings: Case Study in the City of Riyadh

In hot, arid climates, educational buildings often face the challenge of limited outdoor space usage. This research, through comprehensive simulation, aims to propose practical solutions to enhance outdoor thermal comfort, particularly during school break times and student dismissal periods, thereby fostering more comfortable and functional outdoor school environments. That will happen through achieving the main objective of the study, which is evaluating the suggested passive strategies. Riyadh was selected as the case study, and four representative schools were analyzed through simulation and optimization processes to identify key areas for improvement. The research leveraged simulation tools such as Ladybug and Grasshopper in Rhino, highlighting the practicality and impact of this approach. Simulations were performed to assess the existing outdoor thermal conditions using the universal thermal climate index (UTCI) and to pinpoint regions with elevated thermal discomfort. Passive design interventions, such as shading devices and vegetation, were explored and optimized using the Galapagos in Grasshopper. This methodology supports the originality of this research in its integration of simulation tools, such as Ladybug and Grasshopper, with optimization techniques using the Galapagos plugin, specifically applied to the unique site-specific context of educational outdoor environments in a hot, dry climate in Riyadh. Additionally, insights for urban planners and architects demonstrate the possibility of integrating passive design principles to improve the usability and sustainability of outdoor spaces. The findings indicated that fewer apertures in shade devices combined with greater tree canopies might double the effectivity in lowering UTCI values, thereby enhancing thermal comfort, especially during peak summer months.

Temperature and Precipitation Extremes Over Borneo Island: An Integrated Climate Risk Assessment

ABSTRACTGlobal warming has significantly increased the frequency and intensity of extreme events, which have catastrophic consequences for ecosystems and humans. Despite efforts to assess the impact of climate change on the potential risk of Borneo, most research has focused on partial regions, considering short timescales and a limited number of temperature and precipitation extremes indices to quantify the expected climate risks. This study employed a new method of climate risk assessment of Borneo based on the combined changes in various climate parameters. It estimated 23 climate indices at all grid points covering Borneo for three overlapping sub‐periods (1951–1980, 1961–1990, 1991–2020). The modified Mann‐Kendall test was employed to identify grid points exhibiting significant increasing or decreasing trends of each index for each sub‐period. Finally, significant trends of 23 indices were integrated to estimate the potential climate risk indicator (RI) based on the combined changes in various climate parameters for each grid point and sub‐period. Temperature indices showed a clear warming trend across Borneo Island, particularly in the eastern regions, with absolute temperature indices showing an increase of 0.5°C–2.5°C in 1991–2020 compared to the reference period (1951–1980). However, extreme cold temperatures have become less prevalent over the study period. There is a shift from light consecutive rainfall days towards more heavy and short‐duration rainfall events. Therefore, there are indications of intensifying rainfall events over the island's southern half, counterbalanced by drying trends in the northern regions, especially Brunei. The spatial distribution of RI revealed an overall 184% increase in climate risk on the island in recent years (1991–2020) compared to the reference period. The highest rise in RI was in the central east of the island, mostly due to significant increases in rainfall and temperature indices. The findings can inform adaptation initiatives to manage escalating heat and flood risks while guiding additional research to explain further the complex climatic changes occurring in this ecologically and socially vital region.

Identifying critical periods of susceptibility for maternal exposure to biothermal stress and the risks of stillbirth and spontaneous preterm birth in Western Australia.

A few studies investigated critical periods of temperature and the risks of stillbirth and preterm birth. This study aimed to identify critical periods of composite biothermal stress (Universal Thermal Climate Index, UTCI) for stillbirth and spontaneous preterm birth (sPTB). From the Midwives Notification System, 415,271 singleton births between 1st January 2000 and 31st December 2015 were linked to spatiotemporal UTCI in Western Australia. Covariate-adjusted weekly and monthly distributed lag non-linear Cox regression from twelve weeks before conception to birth were performed. Relative to median exposure (14.2 °C), extreme UTCI levels (1st-10th and 90th-99th centiles) were associated with higher hazards of stillbirth and sPTB, especially stronger at lower than higher exposures. Critical susceptible periods at 1st centile (10.2°C) exposure were found during gestational weeks 21-42 with the strongest hazard of 1.14 (95% CI 1.03, 1.27) in the 42nd week for stillbirth and during gestational weeks 26-36 with the strongest hazard of 1.09 (95% CI 1.06, 1.12) in the 36th week for sPTB. Monthly exposure showed a similar pattern but with greater magnitude. Mid to late gestation showed critical susceptible periods of biothermal stress on the birth outcomes, suggesting further studies and timely climate-related healthcare interventions.

Role of Financial Development and ICT in Promoting Inclusive Growth in South Asia: Moderating Effects of Governance

The primary purpose of this study is to compute the index for inclusive growth by taking into account the indicators of poverty, income inequality, labor productivity, life expectancy, job creation, and climate. Calculating the index accomplished by the utilization of principal component analysis in order to acquire weights. Further in the study, the influence of financial development, governance, consumption, and economic growth on inclusive growth for selected south Asian economies (Pakistan, Bangladesh, India, Bhutan, Sri Lanka, and Nepal) is measured. The data is gathered from the World Development Index (WDI), the World Governance Index (WGI), the Internet Subscriber Database, and the International Monetary Fund Development Database. Due to the fact that the data spans from the year 2000 to the year 2023 and that missing values are interpolated through the use of trend projection, a fully balanced panel data sheet has been generated. Through descriptive statistic and VIF, prerequisite information is gathered to see the robustness of model. When it came time to make a decision on the unit root testing, the cross-sectional dependency test utilized to seek guidance. According to the findings, there is no evidence of cross-sectional dependency, and the first generation of unit root tests utilized. The stationarity results of mixed order of integration and T>N indicate the use of panel ARDL. Additionally, in order to see the influence that governance has on inclusive growth in connection to financial development, the moderator effect of governance also inquired. Based on the findings, it is concluded that regulatory quality is not playing a significant moderator role of the financial development towards inclusive growth in selected economies of south Asia. Drawing the inferences from the results of the study it is suggested that to expand the financial inclusion by supporting the digital financial services particularly in marginalized areas.

Demonstrating almost half of cotton fiber quality variation is attributed to climate change using a hybrid machine learning-enabled approach

Understanding the effects of climate change on cotton fiber quality will reduce the risks to production caused by global warming. Machine learning algorithms are effective for forecasting climate impacts on crops. However, the impact of climate change on cotton fiber quality is unclear. Hence, a hybrid machine learning-enabled approach, the Bayesian model average (BMA) method with multiple machine learning algorithms (linear regressor, SVR, RFR, GBDT, LightGBM, and XGBoost) and bootstrap resampling, was developed to explore the impact and screen the important climatic factors affecting various traits of fiber quality. On the basis of fiber quality data from 1033 test stations across Xinjiang, China, from 2016 to 2022, the explained variance for climate change in the hybrid machine learning model was as follows: 44.72 %–50.55 % for white cotton grade, 44.06 %–53.95 % for length, 51.72 %–56.81 % for micronaire, 32.70 %–49.50 % for length uniformity, and 45.66 %–53.09 % for strength in the 1000 bootstrapping samples. In addition, recursive feature elimination with cross-validation (RFECV) was used to select the optimal feature set and calculate the contribution of each feature. The variability in micronaire in the hybrid model was affected primarily by climate factors, such as the daily minimum temperature, rainfall, and wind speed, whereas the other quality traits were affected mainly by radiation-related climatic indicators. The climate during the harvest stage in October had a significant effect on cotton quality, explaining 33.0 % of the variance in white cotton grade, 32.1 % in length, and 48.3 % in fiber strength. Conversely, the climate during the boll opening and early harvest stages in September had a greater influence on micronaire and length uniformity, accounting for 21.4 % and 37.2 % of the variance, respectively. This study highlights that climate change explains nearly 50 % of the variation in fiber quality, with the influence being notably more considerable during the later stages of the cotton growth period. These findings clarify the uncertainty in the impact of climate change on cotton fiber quality considering the uncertainty of the single machine model and model errors. Equally important, this information can be valuable for farmers and growers seeking to improve fiber quality under climate change.

Temperature Trends and Influence of the Base Period Selection on Climate Indices in the Mediterranean Region Over the Period 1961–2020

Temperature Trends and Influence of the Base Period Selection on Climate Indices in the Mediterranean Region Over the Period 1961–2020

Attribution of summer 2022 extreme wildfire season in Southwest France to anthropogenic climate change

Summer 2022 was exceptionally hot and dry in Europe and especially in Southwest France, where the most important wildfires since 1949 had serious environmental and socio-economic impacts. Here we conduct an impact-oriented climate change attribution study by first investigating which climate indices are the most correlated with the burnt area between 2003 and 2022. We find that an index combining soil moisture integrated over 6 months and temperature and vapour pressure deficit integrated over 3 months is correlated with large burnt areas. Using the index developed, we estimate that anthropogenic climate change made climate conditions propitious for wildfire development, such as the ones of July 2022, two times more likely, with a return period of 13 years in the current climate. Our study raises the question of the sustainability of the Landes Forest and stresses the urgent need to mitigate greenhouse gas emissions and adapt to climate change.

Spatiotemporal variations of surface albedo in Central Asia and its influencing factors and confirmatory path analysis during the 21st century

Surface albedo (SA) is crucial for understanding land surface processes and climate simulation. This study analyzed SA changes and its influencing factors in Central Asia from 2001 to 2020, with projections 2025 to 2100. Factors analyzed included snow cover fraction, fractional vegetation cover, soil moisture, average state climate indices (temperature and precipitation), and extreme climate indices (heatwave indices and extreme precipitation indices). Pearson correlation coefficient, geographical convergent cross mapping, and geographical detector were used to quantify the correlation, causal relationship strength, and impact degree between SA and the influencing factors. To address multicollinearity, ridge regression (RR), geographically weighted ridge regression (GWRR), and piecewise structural equation modeling (pSEM) were combined to construct RR-pSEM and GWRR-pSEM models. Results indicated that SA in Central Asia increased from 2001 to 2010 and decreased from 2011 to 2020, with a projected future decline. There is a strong correlation and significant causality between SA and each factor. Snow cover fraction was identified as the most critical factor influencing SA. Average temperature and precipitation had a greater impact on SA than extreme climate indices, with a 1 °C temperature increase corresponding to a 0.004 decrease in SA. This study enhances understanding of SA changes under climate change, and provides a methodological framework for analyzing complex systems with multicollinearity. The proposed models offer valuable tools for studying interrelated factors in Earth system science.

Application of simultaneous evaluation of criteria and alternatives for prioritization of photovoltaic power plant location

AbstractThis research contributes to the ongoing efforts to transition towards clean and renewable energy sources by providing a practical framework for decision‐making in energy resource planning. Hence, a recently developed multi‐criteria decision making technique tool is proposed for simultaneous evaluation of criteria and alternatives (SECA) to find an optimal site for establishing photovoltaic power stations in Iran. The proposed SECA method provides a systematic and comprehensive approach to evaluating renewable energy resources, taking into account technical, economic, environmental, and social factors. The data required for this research has also been collected from the Global Solar Atlas as well as the Iran Meteoritical Organization. The results show that despite the main influence of the solar radiation factor, it should be noted that among all the climatic and environmental indicators, “cloudiness factor” by weight of 0.2339 and “annual temperature” by weight of 0.2320 have been selected as the most important criteria. It was also found that among the ten candidate cities, Zahedan (w = 0.8939), Shahrekord (w = 0.8341) and Kerman (w = 0.8112) have the highest potential for building a solar power plant. The results of the study highlight the potential for solar energy resources to meet Iran's electricity needs in a sustainable manner.

Spatiotemporal variations of global precipitation concentration and potential links to flood-drought events in past 70 years

The evaluation of precipitation events is crucial for predicting severe droughts and floods, particularly in the context of global warming. This study presents a comprehensive spatiotemporal analysis of the global precipitation concentration index (CI) from 1950 to 2020, comparing CI with nine extreme precipitation indices to assess its applicability. The world map was divided into three distinct regions related to flood-drought events using the standardized precipitation index (SPI). Key findings include: 1) CI exhibited significant spatial heterogeneity, with elevated values in seasonally warm coastlines and arid regions. Seasonal changes in CI were not synchronized, particularly lower in Northern Hemisphere winters, while the Southern Hemisphere displayed minor seasonal variation, alongside a long-term growth trend in approximately 36.5 % of the global area. 2) CI's spatiotemporal variations helped delineating regional precipitation patterns, revealing a strong correlation (R2 = 0.87) with the extreme precipitation index R95c, but a negligible relationship with total precipitation (P) (R2 = −0.05). This facilitated the global partitioning into three regions: Humidity Amplification Region (I), Dryness Intensification Region (II), and Climate Fluctuation Region (III). 3) Flood-drought events were potentially linked to variations in CI and P, where In Region I experienced increased flood risk due to rising CI and P, Region II faced heightened drought risk from rising CI and declining P, and Region III showed opposing effects on floods due to reduced CI and increased P, with P variability significantly influencing flood frequency. 4) The main atmospheric circulation factors varied by region, typically including the Antarctic Oscillation (AAO), Atlantic Multi-decadal Oscillation (AMO) and Arctic Oscillation (AO), with AAO being most significant. This research underscores the intricate relationships between climatic indices and hydrological extremes, emphasizing the challenges posed by global warming, atmospheric circulation changes, and data uncertainties in assessing drought and flood disasters.

Results of Economic Tendency Surveys in Nowcasting GDP Growth

The article proves the usefulness of including aggregate information from economic tendency surveys in the system of nowcasting of economic growth. The authors compare the efficiency of vector autoregressive models that include the economic sentiment indicator based on the results of Rosstat business activity and consumer expectations surveys, and the business climate indicator based on the results of the Bank of Russia enterprise monitoring.Statistical testing of time series of composite indicators and GDP volume index confirmed a significant correlation and causality between them, as well as the similarity of their cyclical profiles. Both indices pass cyclical turning points synchronously or ahead of the GDP volume index, their predictive capabilities are enhanced by the early availability of information. The capabilities of GDP growth nowcasting using survey information are assessed in three specifications of the vector autoregressive model with dummy variables. On the in-sample period, the model including two composite indicators and the GDP volume index demonstrates the highest accuracy.The proposed approach allows us to obtain flash estimates of GDP growth prospects that are significantly ahead of the official statistical information

Shifting cold regions streamflow regimes in North America affect flood frequency analysis

ABSTRACT Over threshold flood events in seventy years of data from 202 reference hydrometric stations in Canada and the United States were separated into nival, mixed, and pluvial flood types. While Mann-Kendall trend tests showed few significant trends in flood magnitude, significant changes in flood type fraction were found over time, with annual mean temperature, and with annual precipitation. Nival events decreased in frequency over the seventy-year period in 16% of sites, while mixed and pluvial events increased (5%, 12%). These changes indicate a shift from nival events towards more pluvial dominated systems. Fewer significant changes in flood type fraction were found with analysis against four climate indices. Flood frequency analysis using a combined distribution approach with the three flood types resulted in larger magnitude design flow estimates (median increase of 20 – 30 %) in comparison with the results from considering the data to be a single population.

Courtesy stigma in the families of patients with severe mental disorders: an obstacle to the recovery process?

The general concept of stigma finds wide application in the field of psychiatry by indicating prejudice, stereotypes and discrimination towards patients diagnosed with psychiatric illness. Among the different declinations of this concept, we find courtesy stigma: a form of stigma by association experienced by people in close contact with the stigmatised person.Those who are most likely to experience this type of discrimination are the family members of the psychiatric patient, as they are particularly connected to the person with mental health problems. This experience contributes to increasing levels of expressed emotion (EE), i.e. the index of the emotional climate within the family.This study is based on two samples: a group of users with a diagnosis of severe mental disorder from the Department of Psychiatry of the University of Campania “Luigi Vanvitelli” and another group of family caregivers. This study aimed to: a) assess the impact of courtesy stigma on the levels of recovery and EE perceived by the patient; b) assess the relationship between the latter and the recovery process.To this end, the RAS-DS (Recovery Assessment Scale - Domains and Stages) and LEE (Level of Expressed Emotion Scale) scales were administered to patients, whereas caregivers were administered the CPMI (Stigma scale for Caregivers of People with Mental Illness) scale.Data processing revealed that high levels of courtesy stigma reported by caregivers not only hinder the patients’ recovery process, but also contribute to increasing EE levels according to the patients. Furthermore, as the EE levels perceived by the patients increase, the recovery levels decrease.