Interannual Temperature Variability Research Articles

ТЕРМОХАЛИННЫЕ ХАРАКТЕРИСТИКИ АЗОВСКОГО МОРЯ В ЛЕТНИЙ ПЕРИОД 1989-2021 ГГ. И ВЛИЯНИЕ НА НИХ СТОКА РЕК ДОН И КУБАНЬ

Considerable inter-annual variations of water temperature and salinity in the Azov Sea necessitate their constant monitoring. Following the numerous studies undertaken in this area, this work presents the specific features of inter-annual changes in temperature and salinity of the sea surface layer, averaged for the summer seasons of 1989-2021 both for the Azov Sea (excluding Taganrog Bay) and for its various areas; two representative time spans, 1989-2004 and 2005-2021, have been identified. The first time span was characterized by a relatively wide variation range of the temperature and by its decreased values, as well as by the decrease in the salinity. During the second time span, an increase in the both temperature and salinity of the sea surface layer occurred in the sea and its regions, which was induced by the increase in the air temperatures in the area and by the reduction of the relatively cold Don River runoff after 2005 in the case of the first parameter, and, for the second parameter, it resulted from the decrease in the runoff of the both Don and Kuban Rivers. Correlation analysis of the series of the summer temperature and salinity values by area and for the sea on the whole as they relate to the annual and maximum runoff of the Don River (Razdorskaya) and to the annual runoff of the Kuban River (Krasnodar and the river mouth) for 1989-2021 has shown the absence of the significant relationship with temperature and its presence with salinity both for the sea and for most of its regions at the confidence level of 99 % for the Don River runoff and 95 % for the Kuban River runoff.

Seasonal and interannual variations in sea surface temperature in the Tatar Strait according to satellite data

The aim of the work was to conduct a systematic statistical analysis of the spatial and temporal variability of sea surface temperature (SST) in the waters of the Tatar Strait based on satellite data accumulated in the Sakhalin branch of VNIRO using the TeraScan receiving station for 1998‒2021. It was revealed that in different seasons of the year the SST structure is similar and characterized by the highest values in the southeast and the lowest in the northwest of the strait. An important new result was obtained by expanding of the SST field in terms of the EOF, which is associated with a sharp change in the nature of the time function of the third mode, which occurred in 2013–2014. Such changes can be considered as a climatic shift in the studied area most pronounced in the northwestern part of the strait and near the southwestern coast of Sakhalin Island, where the change was about 1 °C. This circumstance can have a noticeable effect on the state of populations of several species of shrimp and commercial fish.

54 years of microboring community history explored by machine learning in a massive coral from Mayotte (Indian Ocean)

Coral reefs are increasingly in jeopardy due to global changes affecting both reef accretion and bioerosion processes. Bioerosion processes dynamics in dead reef carbonates under various environmental conditions are relatively well understood but only over a short-term limiting projections of coral reef evolution by 2100. It is thus essential to monitor and understand bioerosion processes over the long term. Here we studied the assemblage of traces of microborers in a coral core of a massive Diploastrea sp. from Mayotte, allowing us to explore the variability of its specific composition, distribution, and abundance between 1964 and 2018. Observations of microborer traces were realized under a scanning electron microscope (SEM). The area of coral skeleton sections colonized by microborers (a proxy of their abundance) was estimated based on an innovative machine learning approach. This new method with 93% accuracy allowed analyzing rapidly more than a thousand SEM images. Our results showed an important shift in the trace assemblage composition that occurred in 1985, and a loss of 90% of microborer traces over the last five decades. Our data also showed a strong positive correlation between microborer trace abundance and the coral bulk density, this latter being particularly affected by the interannual variation of temperature and cumulative insolation. Although various combined environmental factors certainly had direct and/or indirect effects on microboring species before and after the breakpoint in 1985, we suggest that rising sea surface temperature, rainfall, and the loss of light over time were the main factors driving the observed trace assemblage change and decline in microborer abundance. In addition, the interannual variability of sea surface temperature and instantaneous maximum wind speed appeared to influence greatly the occurrence of green bands. We thus stress the importance to study more coral cores to confirm the decadal trends observed in the Diploastrea sp. from Mayotte and to better identify the main factors influencing microboring communities, as the decrease of their abundance in living massive stress tolerant corals may have important consequences on their resilience.

Editorial for the Special Issue “Remote Sensing of the Polar Oceans”

This Special Issue gathers papers reporting research on various aspects of the use of satellites for monitoring polar oceans. It includes contributions presenting improvements in the retrieval of sea ice concentration, extent and area, and concerning error information; the interannual and decadal variability of sea surface temperature and sea ice concentration in the Barents Sea; validation and comparison of Arctic salinity products; melt pond retrieval applying a Linear Polar algorithm to Landsat data; the characterization of surface layer freshening from sea surface salinity and coloured detrital matter in the Kara and Laptev Seas; multi-sensor estimations of chlorophyll-a concentrations in the Western Antarctic Peninsula; and enhanced techniques for detection and monitoring of glacier dynamics and iceberg paths.

Differential Responses of Soil Respiration and Its Components to Nitrogen Application in Urban Forests

Understanding the impacts of nitrogen (N) deposition on soil respiration (Rs) and its components (autotrophic respiration (Ra) and heterotrophic respiration (Rh)) in urban forests is crucial for predicting the soil carbon dioxide (CO2) emission and evaluating carbon (C) budget in changing environments. In this study, a three-year-long field manipulation experiment was conducted in two urban forests to assess the effect of N application at three rates (0, 50, and 100 kg N·ha−1·year−1) on Rs components. N application did not alter the seasonal dynamics of Rs and its components. Rs and its components showed nonlinear responses to N application; both Rs and Rh increased in year 1 of N application but decreased in year 3. The Ra/Rs ratio increased by 21% in the low N (50 kg N·ha−1·year−1) plots. The mechanism varied across the years of N application; soil temperature and moisture substantially influenced Ra and Rh under N application. Our results indicated that increasing atmospheric N deposition may inhibit soil CO2 emission, and a higher proportion of soil C is released due to root activities. Interannual variations in temperature and rainfall may help predict the efflux of soil CO2 in urban forests in response to atmospheric N deposition.

Impact of rapid Arctic sea ice decline on China's crop yield under global warming

AbstractFood is the material basis for human survival. Therefore, food security is a top priority for the people's livelihood and the sustainable development and future destiny of human beings. In the context of global warming in recent decades, the Arctic region has experienced more significant temperature anomalies than the midlatitudes due to the "Arctic amplification," and the rate of sea ice reduction has accelerated, which has an important impact on climate change in the middle and high latitudes, especially the frequent occurrence of extreme climate disasters that seriously affect food security and China's agricultural production. However, little research has been conducted on the role of changes in this important system of Arctic sea ice in China's agricultural production. Therefore, this paper analyzes the interannual variability and multi-year trends of Arctic sea ice concentration, CO2, air temperature, precipitation and China's major crop yield data to explore the possible effects and mechanisms of the rapid decrease in Arctic sea ice on China's grain production. From the analysis, it was found that the yield of major grains (rice, maize, wheat and soybean) in China was closely related to the Arctic sea ice anomaly in the previous summer and autumn, and the influence process was primarily through the dynamic process of the Arctic sea ice anomaly affecting the meridional temperature gradient and the positive and negative Arctic Oscillation phases, which in turn affected the air temperature anomalies in Eurasia and China, and finally led to the anomalous changes in Chinese grain yield. Based on this, a prediction model of China's major grain yield was established by stepwise nonlinear multiple regression analysis, which is a good fit and is expected to increase China's major crop yield by 11.4% in 2022 compared with last year. This presents new ideas and methods for future grain yield assessment in China and has far-reaching guidance for the stability and development of national and regional economies worldwide.

The impact of climate change on land productivity. A micro-level assessment for Italian farms

CONTEXTBoth long-term climate trend and interannual climate variability are projected to affect agricultural activities. Actually, major changes in climate patterns have already occurred, affecting crop yields and livestock productivity. OBJECTIVEThis study aims to assess the effects of both interannual temperature and precipitation variability and long-term climate trend on the land productivity of Italian farms. Italy represents an interesting example, as the country is largely affected by the effects of climate change and climate variability, and its agricultural sector shows heterogeneous conditions, including climatic, topographical and socio-economic features. METHODSThe methodological approach considers the effects on land productivity of a set of farm structural and economic characteristics and climate-related variables, by considering farm-level data. A dynamic panel model is estimated via System Generalized Method-of-Moments using a constant panel of 2051 farms observed over the period from 2008 to 2017. Data are extracted from the Italian Farm Accountancy Data Network. RESULTS AND CONCLUSIONSResults suggest that when coming to farm-level data and addressing the changes in the climate in both the long and the medium term, a negative impact is found on LP because of higher temperatures in the colder seasons and in spring, and because of larger spring cumulative rainfall. On the opposite, wetter summers seem to positively impact LP. As regards Mediterranean regions only a positive impact of larger spring cumulative rainfall is found. Besides, managerial and structural farms' characteristics seem to play a major role in influencing farm performance. SIGNIFICANCEThis study represents a first attempt in the direction of estimating the impact of climate change on farms' land productivity, by using micro-data to prevent aggregation biases and controlling for both managerial and structural farms' characteristics.

Inter-annual variability of pre-monsoon surface air temperatures over India using the North American Multi-Model Ensemble models during the global warming era

Inter-annual variability of pre-monsoon surface air temperatures over India using the North American Multi-Model Ensemble models during the global warming era

What Mainly Drives the Interannual Climate Variability Over the Barents‐Kara Seas in Boreal Early Autumn?

AbstractYear‐to‐year forecasts of the Arctic climate conditions have drawn much interest in recent decades. This study investigates the primary drivers responsible for interannual variations (without multi‐decadal trends) of air temperature at 2 m (T2m) and sea ice concentration (SIC) over the Barents‐Kara Seas (BKS) during September from 1950 to 2021. The Arctic Dipole (AD), a leading factor in the BKS variability, plays an important role via influencing large‐scale atmospheric circulations and local thermodynamical feedbacks (i.e., vapor and albedo feedbacks). During a negative AD phase, the positive surface air temperature advection anomalies warm up the BKS region and promote sea ice melting, which in turn maintains the below‐normal sea ice and warmer‐than‐normal conditions via positive vapor and albedo feedbacks. Moreover, our results suggest that the sea surface temperature (SST) variability in the midlatitudes of the North Atlantic in the preceding month could also be an important driver of the SIC variability over the BKS area. The anomalous temperature advections from the surface to the upper troposphere are accompanied by a Rossby wave‐type atmospheric response, which intensifies the energy exchanges between the ocean and the atmosphere. Local SST anomalies associated with the surface heat flux anomalies further persist in the following month and influence sea ice variations due to their long‐lasting memory. A better understanding of the mechanisms of interannual climate variabilities over the BKS region in boreal early autumn is helpful to improving the Arctic regional climate predictions.

Interannual Variability of Tropospheric Moisture and Temperature and Relationships to ENSO Using COSMIC-1 GNSS-RO Retrievals

Abstract Interannual variability of tropospheric moisture and temperature are key aspects of Earth’s climate. In this study, monthly mean specific humidity (q) and temperature (T) variability is analyzed using 12 years of COSMIC-1 (C1) radio occultation retrievals between 60°N and 60°S, with a focus on the tropics. C1 retrievals are relatively independent of the a priori values for q and T within the lower/middle troposphere and upper troposphere/lower stratosphere, respectively. Tropical interannual variability is dominated by El Niño–Southern Oscillation (ENSO). Systematic increases and decreases in zonal mean q and T are observed during the 2009/10 and 2015/16 El Niño events and 2007/08 and 2010/11 La Niña events, respectively. ENSO patterns in q and T are isolated using linear regression, and anomaly magnitudes increase with altitude, reaching a maximum in the upper troposphere. Upper-tropospheric q anomalies expand from the tropics into the midlatitude lower stratosphere, and the T vertical structure is consistent with a moist adiabatic response. C1 results are compared with NCAR’s Whole Atmosphere Community Climate Model (WACCM), forced by observed sea surface temperatures, to evaluate model behavior in an idealized setting. WACCM ENSO variations in q and T generally show consistent behavior with C1 with somewhat smaller magnitudes. Case studies are conducted for major ENSO events during the study period. The spatial variability of q is closely aligned with outgoing longwave radiation (OLR) anomalies. For example, midtropospheric q increases over 100% and OLR decreases over 50 W m−2 over the central Pacific during the 2015/16 El Niño, and substantial regional q and T anomalies are observed throughout the tropics and midlatitudes for each event.

Interannual variability of surface air temperature over indochina peninsula during summer monsoon onset

Interannual variability of surface air temperature over indochina peninsula during summer monsoon onset

Enhanced Intensity of the Interannual Variability of February Surface Air Temperature Over Mid‐ and High‐Latitude Asia Since the Late‐1990s

AbstractThis study investigates the interdecadal variation of the intensity of the interannual variability (IIV) of surface air temperature (SAT) over mid‐ and high‐latitude Asia (MHLA) in winter months (December–February) during 1980–2015 and the role of the Arctic sea‐ice. Results show only the IIV of February SAT is significantly enhanced around the late‐1990s, which partly results from enhanced IIV of the frequency of extreme warm and cold events (FEWE and FECE). The influence of the sea‐ice in the Barents Sea from December to February on the February SAT, FEWE and FECE over MHLA is remarkably enhanced around the late‐1990s. During 1998–2015, the sea‐ice anomalies contribute to the enhanced IIV of February SAT over MHLA by modulating the monthly and synoptic tripole patterns over northern Eurasia, the positive phase of which exhibits an anomalous low over Eurasia north of 60°N and anomalous highs over southern Europe and MHLA. On the monthly average, the increased sea‐ice induces Rossby waves and then a positive phase of the monthly tripole pattern, inducing weakened East Asian trough and Siberian high, enhanced westerlies over 50°–70°N, and resultant decreased blocking high frequency over Eurasia north of 60°N. These anomalies favor increased February SAT over MHLA. On synoptic timescales, the increased blocking over the north side of the Ural Mountains favors more (fewer) extreme positive (negative) phases of the synoptic tripole pattern and hence increased FEWE (decreased FECE) over MHLA, further strengthening the increased SAT. Numerical experiments basically confirm the mechanisms above.

Citizen scientists’ dive computers resolve seasonal and interannual temperature variations in the Red Sea

Dive computers have the potential to provide depth resolved temperature data that is often lacking especially in close to shore, but spatiotemporal assessment of the robustness of this citizen science approach has not been done. In this study, we provide this assessment for the Red Sea, one of the most dived areas in the world. A comparison was conducted between 17 years of minimum water temperatures collected from SCUBA dive computers in the northern Red Sea (23–30° N, 32–39.4° E), satellite-derived sea surface temperatures from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) optimal interpolation product, and depth-banded monthly mean in-situ temperature from the TEMPERSEA dataset, which incorporates data originating from several in-situ recording platforms (including Argo floats, ships and gliders). We show that dive computer temperature data clearly resolve seasonal patterns, which are in good agreement in both phase and amplitude with OSTIA and TEMPERSEA. On average, dive computer temperatures had an overall negative bias of (–0.5 ± 1.1) °C compared with OSTIA and (–0.2 ± 1.4) °C compared with TEMPERSEA. As may be expected, increased depth-related biases were found to be associated with stratified periods and shallower mixed layer depths, i.e., stronger vertical temperature gradients. A south-north temperature gradient consistent with values reported in the literature was also identifiable. Bias remains consistent even when subsampling just 1% of the total 9310 dive computer datapoints. We conclude that dive computers offer potential as an alternative source of depth-resolved temperatures to complement existing in situ and satellite SST data sources.

Enhanced Interannual Variability in Temperature during the Last Glacial Maximum

Abstract Using all relevant climate experiments archived in phases 3 and 4 of the Paleoclimate Modeling Intercomparison Project (PMIP3/4), we examine the interannual variability change in global-scale surface air temperature and associated physical mechanisms during the Last Glacial Maximum (LGM). The results show that relative to the preindustrial period, the LGM interannual temperature variability increased by 20% globally, which was mainly attributed to the large-scale increase in the meridional temperature gradient, especially at midlatitudes. Larger magnitudes of change occurred in areas where the underlying surface properties, such as the surface altitude, land–sea distribution, and ice sheet extent, differed from those in the preindustrial period. In addition, the relationship between changes in the meridional temperature gradient and the interannual temperature variability became closer in the winter hemisphere. In the tropical land regions, changes in interannual temperature variability are mainly related to the adjustment of latent and sensible heat fluxes during the LGM.

Tree-ring anatomy of Pinus cembra trees opens new avenues for climate reconstructions in the European Alps

Tree rings form the backbone of high-resolution palaeoclimatology and represent one of the most frequently used proxy to reconstruct climate variability of the Common Era. In the European Alps, reconstructions were often based on tree-ring width (TRW) and maximum latewood density (MXD) series, with a focus on European larch. By contrast, only a very limited number of dendroclimatic studies exists for long-lived, multi-centennial Pinus cembra, despite the widespread occurrence of the species at treeline sites across the European Alps. This lack of reconstructions can be ascribed to the difficulties encountered in past studies in extracting a robust climate signal from TRW and MXD chronologies. In this study, we tested various wood anatomical parameters from P. cembra as proxies for the reconstruction of past air temperatures. To this end, we measured anatomical cell parameters and TRW of old-growth trees from the God da Tamangur forest stand, known for being the highest pure, and continuous P. cembra forest in Europe. We demonstrate that several wood anatomical parameters allow robust reconstruction of past temperature variability at annual to multidecadal timescales. Best results are obtained with maximum latewood radial cell wall thickness (CWTrad) measured at 40 μm radial band width. Over the 1920–2017 period, the CWTrad chronology explains 62 % and >80 % of interannual and decadal variability of air temperatures during a time window corresponding roughly with the growing season. These values exceed those found in past work on P. cembra and even exceed the values reported for MXD chronologies built with L. decidua and hitherto considered the gold standard for dendroclimatic reconstructions in the European Alps. The wood anatomical analysis of P. cembra records therefore unveils a dormant potential and opens new avenues for a species that has been considered unsuitable for climate reconstructions so far.

Accounting for snowpack and time-varying lags in statistical models of stream temperature

Water temperature plays a primary role in driving ecological processes in streams due to its direct impact on biogeochemical cycles and the physiological processes of stream fauna, such as growth, development, and the timing of life history events. Streams influenced by snowpack melt are generally cooler in the summer and demonstrate less sensitivity to climate variability in what is commonly referred to as “climate buffering”. Despite the substantial influence of snowpack on stream temperature and expected changes in snowpack accumulation and melt timing with climate change, methods for representing snowpack in statistical models for stream temperature have not been well explored. In this investigation, we quantified the extent of stream temperature buffering in free-flowing streams across a geographically diverse region in the Pacific Northwest USA. We demonstrated that statistical models of daily mean stream temperature can be improved by explicitly accounting for temporal variability in a small number of climate covariates believed to be mechanistically related to stream temperature. Our novel statistical approach included as predictors combinations and interactions between the following variables: (1) air temperature, (2) lagged air temperature (where the lag duration varied according to its relationship with flow on a given day at that site), (3) flow, (4) snowpack in the upstream catchment, and (5) day of year. We found that sites with substantial snow influence were associated with increased air temperature buffering during the warm season and longer air temperature lags (>30 days during spring high flows and ∼ 10 days during late summer low flows) compared to sites where precipitation predominantly fell as rain (<6 days year-round). By accounting for snowpack and temporal variation in lagged heat transfer processes, our models were able to accurately predict seasonal patterns and interannual variability in stream temperature in validation data from years not used in model fits using publicly available data sources (average RMPSE ∼ 0.80).

Interannual and Decadal Variability of Sea Surface Temperature and Sea Ice Concentration in the Barents Sea

Sea ice loss and accelerated warming in the Barents Sea have recently been one of the main concerns of climate research. In this study, we investigated the trends and possible relationships between sea surface temperature (SST), sea ice concentration (SIC), and local and large-scale atmospheric parameters over the last 39 years (1982 to 2020). We examined the interannual and long-term spatiotemporal variability of SST and SIC by performing an empirical orthogonal function (EOF) analysis. The SST warming rate from 1982 through 2020 was 0.35 ± 0.04 °C/decade and 0.40 ± 0.04 °C/decade in the ice-covered and ice-free regions, respectively. This climate warming had a significant impact on sea-ice conditions in the Barents Sea, such as a strong decline in the SIC (−6.52 ± 0.78%/decade) and a shortening of the sea-ice season by about −26.1 ± 7.5 days/decade, resulting in a 3.4-month longer summer ice-free period over the last 39 years. On the interannual and longer-term scales, the Barents Sea has shown strong coherent spatiotemporal variability in both SST and SIC. The temporal evolution of SST and SIC are strongly correlated, whereas the Atlantic Multidecadal Oscillation (AMO) influences the spatiotemporal variability of SST and SIC. The highest spatial variability (i.e., the center of action of the first EOF mode) of SST was observed over the region bounded by the northern and southern polar fronts, which are influenced by both warm Atlantic and cold Arctic waters. The largest SIC variability was found over the northeastern Barents Sea and over the Storbanken and Olga Basin. The second EOF mode revealed a dipole structure with out-of-phase variability between the ice-covered and ice-free regions for the SST and between the Svalbard and Novaya Zemlya regions for SIC. In order to investigate the processes that generate these patterns, a correlation analysis was applied to a set of oceanic (SST) and atmospheric parameters (air temperature, zonal, and meridional wind components) and climate indices. This analysis showed that SST and SIC are highly correlated with air temperature and meridional winds and with two climate indices (AMO and East Atlantic Pattern (EAP)) on an interannual time scale. The North Atlantic Oscillation (NAO) only correlated with the second EOF mode of SST on a decadal time scale.

Developing a Climate Prediction System over Southwest China Using the 8-km Weather Research and Forecasting (WRF) Model: System Design, Model Calibration, and Performance Evaluation

Abstract A high-resolution, short-term climate prediction system for summer (June–July–August) climate over Southwest China has been developed using the Weather Research and Forecasting (WRF) Model nested with a global climate prediction system (PCCSM4). The system includes 12 ensemble members generated by PCCSM4 with different initial conditions, and the finest horizontal resolution of WRF is 8 km. This study evaluates the ability of the WRF Model to predict summer climate over Southwest China, focusing on the system design, model tuning, and evaluation of baseline model performance. Sensitivity simulations are first conducted to provide the optimal model configuration, and the model performance is evaluated against available observational data using reforecast simulations for 1981–2020. When compared to PCCSM4, the WRF Model shows major improvements in predicting the spatial distribution of major variables such as 2-m temperature, 10-m wind speed, and precipitation. WRF also shows better skill in predicting interannual temperature variability and extreme temperature events, with higher anomaly correlation coefficients. However, large model biases remain in seasonal precipitation anomaly predictions. Overall, this study highlights the potential advantages of using the high-resolution WRF Model to predict summer climate conditions over Southwest China.

Global forests are influenced by the legacies of past inter-annual temperature variability

Inter-annual climate variability (hereafter climate variability) is increasing in many forested regions due to climate change. This variability could have larger near-term impacts on forests than decadal shifts in mean climate, but how forests will respond remains poorly resolved, particularly at broad scales. Individual trees, and even forest communities, often have traits and ecological strategies—the legacies of exposure to past variable conditions—that confer tolerance to subsequent climate variability. However, whether local legacies also shape global forest responses is unknown. Our objective was to assess how past and current climate variability influences global forest productivity. We hypothesized that forests exposed to large climate variability in the past would better tolerate current climate variability than forests for which past climate was relatively stable. We used historical (1950–1969) and contemporary (2000–2019) temperature, precipitation, and vapor pressure deficit (VPD) and the remotely sensed enhanced vegetation index (EVI) to quantify how historical and contemporary climate variability relate to patterns of contemporary forest productivity. Consistent with our hypothesis, forests exposed to large temperature variability in the past were more tolerant of contemporary temperature variability than forests where past temperatures were less variable. Forests were 19-fold times less sensitive to contemporary temperature variability where historical inter-annual temperature variability was 0.66 °C (two standard deviations) greater than the global average historical temperature variability. We also found that larger increases in temperature variability between the two study periods often eroded the tolerance conferred by the legacy effects of historical temperature variability. However, the hypothesis was not supported in the case of precipitation and VPD variability, potentially due to physiological tradeoffs inherent in how trees cope with dry conditions. We conclude that the sensitivity of forest productivity to imminent increases in temperature variability may be partially predictable based on the legacies of past conditions.

Polar stratospheric nitric acid depletion surveyed from a decadal dataset of IASI total columns

Abstract. In this paper, we exploit the first 10-year data record (2008–2017) of nitric acid (HNO3) total columns measured by the IASI-A/MetOp infrared sounder, characterized by an exceptional daily sampling and a good vertical sensitivity in the lower-to-mid stratosphere (around 50 hPa), to monitor the relationship between the temperature decrease and the observed HNO3 loss that occurs each year in the Antarctic stratosphere during the polar night. Since the HNO3 depletion results from the formation of polar stratospheric clouds (PSCs), which trigger the development of the ozone (O3) hole, its continuous monitoring is of high importance. We verify here, from the 10-year time evolution of HNO3 together with temperature (taken from reanalysis at 50 hPa), the recurrence of specific regimes in the annual cycle of IASI HNO3 and identify (for each year) the day and the 50 hPa temperature (“drop temperature”) corresponding to the onset of strong HNO3 depletion in the Antarctic winter. Although the measured HNO3 total column does not allow for the uptake of HNO3 by different types of PSC particles along the vertical profile to be differentiated, an average drop temperature of 194.2 ± 3.8 K, close to the nitric acid trihydrate (NAT) existence threshold (∼ 195 K at 50 hPa), is found in the region of potential vorticity lower than −10 × 10−5 Km2kg-1s-1 (similar to the 70–90∘ S equivalent latitude region during winter). The spatial distribution and interannual variability of the drop temperature are investigated and discussed. This paper highlights the capability of the IASI sounder to monitor the evolution of polar stratospheric HNO3, a key player in the processes involved in the depletion of stratospheric O3.