Temperature Anomalies Research Articles

Ensemble Predictability of Week 3/4 Precipitation and Temperature over the United States via Cluster Analysis of the Large-Scale Circulation

Abstract Forecasting the week 3/4 period presents many challenges, resulting in a need for improvements to forecast skill. At this distance from initial conditions, numerical models struggle to present skillful forecasts of temperature, precipitation, and associated extremes. One approach to address this is to utilize more predictable large-scale circulation regimes to make forecasts of temperature and precipitation anomalies, using the association between the regimes and surface weather obtained from reanalysis products. This study explores the utility of k-means cluster analysis on geopotential heights and their ability to make skillful regime predictions in the week 3/4 period. Using 14-day running means of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) 500-hPa geopotential heights for the wintertime December–February (DJF) period, circulation regimes are identified using k-means clustering. Each period is assigned a cluster number, allowing the compositing of any reanalysis or observation variable to form cluster maps. Maps of 500-hPa height, 2-m temperature, precipitation, and storm-track anomalies are some of the variables composited. The utility of these relationships in a dynamical forecast setting is tested via Global Ensemble Forecast System v12 (GEFSv12) hindcasts and real-time ensemble suite forecasts. Week 3/4 deterministic and probabilistic experimental forecasts are then derived from cluster assignments using several methods. We find, via a conditional skill analysis, forecasts strongly correlated with a cluster exhibit greater skill for both dynamical model and cluster-derived forecasts. Our preliminary results represent a step forward to aid forecasters make more skillful assessments of the circulation regime and its associated surface weather for this challenging forecast time scale. Significance Statement Our paper links the local statistics of 14-day precipitation and temperature within the United States to very broad preferred patterns of winds and pressure over the wide Pacific–North American region. Such patterns, known as “circulation regimes,” provide the background that heavily influences local surface conditions. We find that forecasts for which midatmospheric anomalies are closely aligned with one or more circulation regimes are better at predicting the U.S. weather probabilities than other week 3–4 forecasts. A tool based on this finding identifies forecasts for which confidence is higher. Future work will be designed to further exploit the links between circulation regimes and weather to improve the accuracy of week 3–4 forecasts.

Cross-Time-Scale Analysis of Year-Round Atmospheric Circulation Patterns and Their Impacts on Rainfall and Temperatures in the Iberian Peninsula

Abstract This study presents a framework to assess climate variability and change through atmospheric circulation patterns (CPs) and their link with regional processes across time scales. We evaluate the CP impacts on daily rainfall and maximum and minimum temperatures in the Iberian Peninsula using sea level pressure (SLP) during 1950–2022. Different sensitivity analyses are performed, employing multiple spatial domains and number of patterns. An optimal classification is found in midlatitudes, centered over the Mediterranean basin and covering part of the North Atlantic Ocean, which can identify atmospheric configurations significantly related to discriminated rainfall and temperature anomalies, with clear seasonal behavior. The temporal variability of CPs is studied across time scales showing, e.g., that transitions between patterns are faster in autumn and spring, and that CPs exhibit distinct temporal variability at intraseasonal, seasonal, interannual, and decadal scales, including significant long-term trends on their frequency. CPs influence temperature and precipitation variations throughout the year. The winter season exhibits the largest atmospheric circulation variability, while the summer is dominated by persistent high-pressure structures—the subtropical Azores high—leading to warm and dry conditions. Based on an interannual correlation analysis, some CPs are significantly associated with the North Atlantic Oscillation (NAO), stronger during winter, indicating the NAO modulation on the regional-to-local climatic features. Overall, this approach arises as a dynamic cross-time-scale framework that can be adapted to specific user needs and levels of regional detail, being useful to study climate drivers for climate change and to perform a process-based evaluation of climate models.

No Evidence of Winter Warming in Eurasia Following Large, Low-Latitude Volcanic Eruptions during the Last Millennium

Abstract We critically reexamine the question of whether volcanic eruptions cause surface warming over Eurasia in winter, in the light of recent modeling studies that have suggested internal variability may overwhelm any forced volcanic response, even for the very largest eruptions during the Common Era. Focusing on the last millennium, we combine model output, instrumental observations, tree-ring records, and ice cores to build a new temperature reconstruction that specifically targets the boreal winter season. We focus on 20 eruptions over the last millennium with volcanic stratospheric sulfur injections (VSSIs) larger than the 1991 Pinatubo eruption. We find that only 7 of these 20 large events are followed by warm surface temperature anomalies over Eurasia in the first posteruption winter. Examining the 13 events that show cold posteruption anomalies, we find no correlation between the amplitude of winter cooling and VSSI mass. We also find no evidence that the North Atlantic Oscillation is correlated with VSSI in winter, a key element of the proposed mechanism through which large, low-latitude eruptions might cause winter warming over Eurasia. Furthermore, by inspecting individual eruptions rather than combining events into a superposed epoch analysis, we are able to reconcile our findings with those of previous studies. Analysis of two additional paleoclimatic datasets corroborates the lack of posteruption Eurasian winter warming. Our findings, covering the entire last millennium, confirm the findings of most recent modeling studies and offer important new evidence that large, low-latitude eruptions are not, in general, followed by significant surface wintertime warming over Eurasia.

Future climate-driven escalation of Southeastern Siberia wildfires revealed by deep learning

The Southeastern Siberia (SES) region has recently experienced increasingly extensive wildfires in spring, which have threatened its large carbon sequestration capacity from vast forests and peatlands. However, the underlying mechanisms propelling the increased fires and their potential responses to future climate change remain unclear. Here, by using reanalysis data and climate model output together with a deep learning model, we explore the relationship between positive-phase North Atlantic Tripole (NAT) sea-surface temperature anomalies and SES wildfire increases and project the future trend in SES wildfire intensities under climate change. We found that the positive-phase April NAT enhances the Siberian anticyclone, causing increased temperatures and snowmelt via strengthened transport of warm-air advection into the SES region. The latter process heightens the exposure of local high-density peatlands to favorable conditions for fire ignition and leads to more intensive wildfire incidents. We further demonstrate that the projected NAT variations can drive interdecadal changes in future April SES wildfires. With future phase shifting of NAT modes under global warming, the regionally averaged burned area in SES could be increased by 47–62% under different warming scenarios from 1982–2014 to 2015–2100. Our findings reveal the climate-driven escalation of future wildfires in SES in the context of global warming and call for active and urgent fire management strategies to mitigate the fire risk.

Using rare event algorithms to understand the statistics and dynamics of extreme heatwave seasons in South Asia

Abstract Computing the return times of extreme events and assessing the impact of climate change on such return times is fundamental to extreme event attribution studies. However, the rarity of such events in the observational record makes this task a challenging one, even more so for ‘record-shattering’ events that have not been previously observed at all. While climate models could be used to simulate such extremely rare events, such an approach entails a huge computational cost: gathering robust statistics for events with return time of centuries would require a few thousand years of simulation. In this study, we use an innovative tool, rare event algorithm, that allows us to sample numerous extremely rare events at a much lower cost than direct simulations. We employ the algorithm to sample extreme heatwave seasons, corresponding to large anomalies of the seasonal average temperature, in a heatwave hotspot of South Asia using the global climate model Plasim. We show that the algorithm estimates the return levels of extremely rare events with much greater precision than traditional statistical fits. It also enables the computation of various composite statistics, whose accuracy is demonstrated through comparison with a very long control run. In particular, our results reveal that extreme heatwave seasons are associated with an anticyclonic anomaly embedded within a large-scale hemispheric quasi-stationary wave-pattern. Additionally, the algorithm accurately represents the intensity-duration-frequency statistics of sub-seasonal heatwaves, offering insights into both seasonal and sub-seasonal aspects of extreme heatwave seasons. This innovative approach could be used in extreme event attribution studies to better constrain the changes in an event’s probability and intensity with global warming, particularly for events with return times spanning centuries or millennia.

Impact of land use land cover changes on urban temperature in Jakarta: insights from an urban boundary layer climate model

Urbanization is one of the important drivers of increasing local temperatures. As cities and urban areas evolve, extensive land use and land cover (LULC) changes alter the physical characteristics of surface materials. This modification results in reduced evapotranspiration rates, ultimately contributing to higher surface and air temperatures. This study investigated the impact of urbanization on urban temperature in Jakarta. Urban temperature was simulated for a 20-year time period (1995–2014) by the urban boundary layer climate model UrbClim, using LULC data for both 1995 and 2014. Temperature changes were analysed by assessing the temperature anomaly across different LULC change classes divided into four main classes namely no built-up changes (BB), no green spaces changes (GG), built-up to green spaces (BG), and green spaces to built-up (GB). The study revealed that the conversion of green spaces to built-up areas (GB) had the most significant impact on the increase in air temperature. This was indicated by the mean values of the temperature anomaly of GB of about 0.24°C followed by GG, BB, and BG with the mean values of the temperature anomaly of about 0.20°C, 0.19°C, 0.17°C, respectively. The different temperature anomalies of the LULC change classes indicate that green spaces have an important role in maintaining local climate. Hence, it is important for local government to effectively manage the composition, the quantity, as well as the distribution of green spaces within a city. By looking at temperature anomalies of LULC change classes, the present study provides an alternative approach to many existing methods that provide general information about temperature changes, without specifically analyzing the effects of LULC transformations.

Baleen stable isotopes reveal climate-driven behavioural shifts in North Atlantic fin whales

Climate variability impacts the structure and functioning of marine ecosystems and can trigger behavioural responses in organisms. We investigated whether such variability modulates diet and migration in the North Atlantic fin whale (Balaenoptera physalus). To reconstruct the dietary and migratory behaviours over time, we conducted stable isotope analysis of nitrogen (δ15N) and carbon (δ13C) along baleen plates from 29 fin whales sampled off southwestern (SW) Iceland in summer. We estimated a baleen growth rate of 16.1 ± 2.5 cm per year from the stable isotope oscillations observed along the baleens. We also assigned a deposition date for each baleen segment, thus obtaining isotopic sequential time series. We then assessed the potential association of these time series with the main climate patterns of the North Atlantic basin. Baleen δ15N and δ13C values are associated with the North Atlantic Oscillation (NAO) and the Atlantic Multidecadal Oscillation (AMO). During high AMO and low NAO periods, which tend to decrease krill abundance, there is an increase in both the mean and standard deviation of baleen δ15N values, suggesting that fin whales shift to higher trophic resources and expand their dietary niche. Additionally, high AMO periods, which relate to positive temperature anomalies, lead to a decrease in baleen δ13C values, suggesting that fin whales adjust their migratory routes and destinations towards higher latitudes. Significant variation in isotopic niche width between years also reflected these dietary and migratory behavioural shifts. This highlights the plasticity of the North Atlantic fin whale behaviour, a trait likely to strengthen the resilience of the species within the current context of rapid and intense climate variability.

Heat impacts on an aging society: a spatio-temporal analysis of heatstroke deaths in Japan

Abstract Climate change, especially through heatwaves, significantly affects human health and is a growing global concern. Concurrently, population aging is occurring worldwide, with many countries experiencing an increase in their elderly populations. As the elderly are particularly susceptible to extreme temperatures and unevenly distributed due to internal migration of younger populations, a spatio-temporal analysis integrating temperature changes and demographic data is essential. This study focuses on Japan, a super-aged society where over 25% of the total population is 65 years or older. We examined the effects of climate and the proportion of the elderly population on heatstroke deaths through spatio-temporal analysis within a Bayesian framework. We estimated the annual relative risk of heatstroke-related deaths at the prefecture level from 2008 to 2019. The results indicate a strong spatial autocorrelation in heatstroke deaths across Japan. The spatio-temporal interaction model was the best-performing, showing that regional and temporal variations significantly impact heatstroke mortality. In this model, a one-degree increase in temperature anomaly was linked to a 0.35 (95% CI 0.25 to 0.46) times higher odds of heatstroke deaths, while a 1% increase in the population aged 65 years or older was associated with 4.85 (95% CI 0.92 to 8.65) times higher odds. We found that not only metropolitan areas but also rural areas, such as the Tohoku and Shikoku regions, face a high risk of heatstroke, emphasizing the need to address the challenges in rural communities. Our study highlights the necessity of integrating temperature changes and demographic data in a spatio-temporal context for heatstroke risk assessment. It demonstrates the profound effects of temperature anomalies and the proportion of the elderly population on heatstroke mortality. This research framework could be applicable to other countries experiencing aging and heatwave issues, aiding in the development of targeted public health interventions.

Synchronous decadal climate variability in the tropical Central Pacific and tropical South Atlantic

Pantropical climate interactions across ocean basins operate on a wide range of timescales and can improve the accuracy of climate predictions. Here, we show in observations that Central Pacific (CP) El Niño-like sea surface temperature (SST) anomalies have coevolved with tropical South Atlantic SST anomalies on a quasi-decadal (~10-year) timescale over the past seven decades. During the austral autumn–winter season, decadal warm SSTs in the tropical CP effectively induce tropical SST cooling in the South Atlantic, mainly by strengthening the South Atlantic subtropical anticyclone via an extratropical atmospheric wave teleconnection in the southern hemisphere. Partially coupled pacemaker simulations corroborate the observational findings, indicating that tropical CP decadal SSTs play a primary pacing role, while Atlantic feedback is of secondary importance throughout the study period. Our results suggest that the tropical CP could be an important source of decadal predictability for tropical South Atlantic SST and the surrounding climate.

Основные особенности климатических условий зимнего сезона 2023/2024 гг. по данным мониторинга и прогнозов

The main results of the comprehensive analysis of the Northern Hemisphere large-scale atmospheric circulation features are presented for the 2023/2024 winter season. Skill scores of the consensus forecast for the 2023/2024 Northern Eurasia winter season are discussed in the context of reproducing the temperature and humidity regime. The qualitative analysis of the multimodel forecast from WMO’s Lead Center and the consensus forecast issued by NEACOF for seasonal anomalies in air temperature and precipitation for the winter of 2023/2024 resulted in the conclusion of the superior accuracy of the consensus forecast. This forecast was based on the data of three Russian models SL-AV, MGO, and INM with equal weighting coefficients.

ҚАЗАҚСТАННЫҢ ШЫҒЫСЫНДАҒЫ КЛИМАТТЫҢ ЗАМАНАУИ ӨЗГЕРУЛЕРІ

Currently, the problem of climate change has not only alarmed meteorologists, but has also become a large-scale topic of public attention in general. One of the Sustainable Development Goals proposed by the United Nations is the Fight against climate change (SDG-13CIS-13), within the framework of which large-scale projects and a number of activities are being carried out in the country. Modern climatic changes in Kazakhstan lead to an increase in air temperature and precipitation, as well as the duration, intensity and frequency of hydrometeorological phenomena. It is important to understand the dynamics of their changes, to determine the patterns of occurrence. During the research in 1942...2022. The meteorological stations Ust-Kamenogorsk, Semey, Shemonaiha, Katon-Karagai, Kurchum, Zaisan, Aksuat in East Kazakhstan have a tendency to increase air temperature by 0,2...0,4 0C every 10 years. There was a tendency for precipitation to increase by 1,6...7,7 mm every 10 years at Zaisan, Shemonaiha and Semey stations, and by 4,1...5,9 mm every 10 years at other stations. Anomalies in air temperature and precipitation over the past decade have shown a positive trend.According to the Mann-Kendall test, the value of the air temperature change was higher at all stations in February-March and August. The value of precipitation changes in the spring and autumn months is small, and at Zaisan, Shemonaiha and Semey stations it was higher in the cold season.

Temporal connections between extreme precipitation and humid heat

Abstract Individually, extreme humid heat and extreme precipitation events can trigger widespread socioeconomic impacts which disproportionately affect vulnerable populations. These impacts might become greater when both events occur in close temporal proximity, for example if emergency responses to heat stress casualties are hindered by flooded roads. Improved understanding of the probabilities and physical mechanisms associated with these events’ temporal compounding might uncover causal interrelationships offering avenues for improving early warning systems and projecting changes in a warmer climate. We explore sequential humid heat and rainfall relationships during the local summer season, identifying two classes of temporal relationships. We find that high wet bulb temperature (WBT) anomalies in most mid- to high-latitude and tropical regions are preceded by anomalously low precipitation. In contrast, hot and dry subtropical regions generally experience elevated WBTs during and, to a somewhat lesser extent, before extreme precipitation events. High WBT events are followed by positive precipitation anomalies in many land regions.

Seasonal prediction of North Atlantic sea surface temperature anomalies using machine learning method of LSTM

Seasonal prediction of North Atlantic sea surface temperature anomalies using machine learning method of LSTM

Interannual Covariation of the North American and African Summer Monsoons

ABSTRACTThe North American summer monsoon (NASM) and the North African summer monsoon (NAFSM) are two vital subsystems of the global monsoon. To date, the potential inter‐monsoon relationship between the NASM and NAFSM has not been fully understood. To fill this gap, we investigate the NASM–NAFSM relationship on the interannual timescale during the period of 1979–2022. Based on statistical methods (including correlation, empirical orthogonal function and cross wavelet analyses), we identify a noteworthy interannual covariation of the NASM and NAFSM. This observed NASM–NAFSM covariation can be explained by atmospheric circulation anomalies associated with sea surface temperature (SST) anomalies in the tropical central‐eastern Pacific and tropical Atlantic, suggesting the critical roles of tropical Pacific–Atlantic SST anomalies in shaping the NASM–NAFSM covariation. The results of Coupled Model Intercomparison Project Phase 6 (CMIP6) models indicate that a model's ability to simulate the NASM–NAFSM covariation tends to be related to its ability to reproduce the modulating effects of the tropical Pacific–Atlantic SST anomalies. These results have potential implications for seasonal forecasts of the NASM and NAFSM variations, suggesting that the NASM and NAFSM can be considered simultaneously in climate predictions.

A framework for reconstructing marine heatwaves from individual foraminifera in sedimentary archives

Marine heatwaves (MHWs) are warm sea surface temperature (SST) anomalies with substantial ecological and economic consequences. Observations of MHWs are based on relatively short instrumental records, which limit the ability to forecast these events on decadal and longer timescales. Paleoclimate reconstructions can extend the observational record and help to evaluate model performance under near future conditions, but paleo-MHW reconstructions have received little attention, primarily because marine sediments lack the temporal resolution to record short-lived events. Individual foraminifera analysis (IFA) of paleotemperature proxies presents an intriguing opportunity to reconstruct past MHW variability if strong relationships exist between SST distributions and MHW metrics. Here, we describe a method to test this idea by systematically evaluating relationships between MHW metrics and SST distributions that mimic IFA data using a 2000-member linear inverse model (LIM) ensemble. Our approach is adaptable and allows users to define MHWs based on multiple duration and intensity thresholds and to model seasonal biases in five different foraminifera species. It also allows uncertainty in MHW reconstructions to be calculated for a given number of IFA measurements. An example application of our method at 12 north Pacific locations suggests that the cumulative intensity of short-duration, low-intensity MHWs is the strongest target for reconstruction, but that the error on reconstructions will rely heavily on sedimentation rate and the number of foraminifera analyzed. This is evident when a robust transfer function is applied to new core-top oxygen isotope data from 37 individual Globigerina bulloides at a site with typical marine sedimentation rates. In this example application, paleo-MHW reconstructions have large uncertainties that hamper comparisons to observational data. However, additional tests demonstrate that our approach has considerable potential to reconstruct past MHW variability at high sedimentation rate sites where hundreds of foraminifera can be analyzed.

Comparing MJO Intensity over the Tropical Western Pacific during Mega and Equatorial La Niña Winters

Abstract This study investigated variations in the Madden–Julian oscillation (MJO) behavior between two types of La Niña winters: mega and equatorial La Niñas. Results of this work show that in contrast to mega conditions, accompanied by more abundant intraseasonal column-integrated moisture anomalies over the planetary boundary layer, intensities of intraseasonal outgoing longwave radiation anomalies are stronger over the tropical western Pacific (WP) at MJO phases 5–6 under equatorial conditions. The occurrence of the moisture anomaly change averaged over the tropical WP is supported by a distinct moisture tendency difference. Moisture budget and multiscale interaction diagnoses underscore the pivotal effect of an area-averaged vertical gradient change in low-frequency background moisture in driving such tendency change. Considering notable MJO convection variations, the teleconnections associated with MJO phases 5–6 over East Asia (EA) were also explored, including warmer surface air temperature anomalies and stronger positive geopotential height anomalies at 200 hPa on the intraseasonal time scale under equatorial conditions. Results from a linear baroclinic model demonstrate that such teleconnection changes could be effectively explained by the linear response to the MJO’s diabatic heating anomaly. Roles of mean state and MJO itself variations in the linear response change are also discussed. These findings provide novel insights into MJO activity and offer potential improvements for subseasonal forecasting in EA. Significance Statement The relationship between El Niño–Southern Oscillation (ENSO) and MJO has been extensively explored recently. However, variations in the MJO behavior and its climate effects under mega and equatorial La Niña winters have not been thoroughly investigated. In this work, we utilized reanalysis datasets and an idealized linear baroclinic model to address these questions. We observed more robust and abundant intraseasonal convection activity and planetary boundary layer–integrated moisture anomaly averaged over the tropical WP at MJO phases 5–6 under equatorial conditions than mega conditions, which is closely linked with the vertical gradient change in low-frequency background moisture. Additionally, MJO-related teleconnections also exhibit some changes. This work may provide a new perspective on understanding the relationship between the MJO and ENSO and offer potential improvements for the subseasonal forecast.

Atmospheric Precursors of Skillful SST Prediction in the Northeast Pacific

Abstract Forecasts of sea surface temperature anomalies (SSTAs) provide essential information to stakeholders of marine resources in coastal ecosystems, such as the California Current Large Marine Ecosystem (CCLME), at management-relevant monthly-to-annual time scales. Diagnosing dynamical sources of predictability and the mechanisms differentiating skill among forecasts is required for verification and improvement in operational forecasting systems. Using retrospective forecasts (1982–2020) from a four-member subset of the North American Multi-Model Ensemble (NMME), we evaluate the conditional skill of SSTA forecasts in the CCLME at monthly resolution for lead times up to 10.5 months. Forecasts from ensemble members with relatively small SSTA errors at shorter lead times retain higher skill at longer lead times, with the most substantial and long-lasting increases for forecasts initialized in the fall and early spring. The “best” low-error SSTA forecasts are characterized by increased skill in the prediction of North Pacific atmospheric circulation [sea level pressure (SLP) and 200-hPa geopotential height] the month prior to the evaluation of SSTA errors in the CCLME and exhibit more realistic progressions of anomalous SLP. The Pacific meridional mode (PMM) emerges as a diagnostic of skillful North Pacific atmosphere–ocean coupling, as forecasts that correctly simulate the PMM and its associated SLP variability increase the SSTA prediction skill in the CCLME in the fall through spring. Predictable coupled ocean–atmosphere modes provide a target for enhancing predictability with early detection of the onset of a deterministic progression emerging from stochastic atmospheric variability. Significance Statement Global forecast systems provide near-term climate predictions that inform the management of marine resources, such as those of the California Current Large Marine Ecosystem. In this study, we probe the processes which lead forecasts to succeed or fail at predicting sea surface temperatures in the California Current at seasonal time scales among retrospective forecasts from the North American Multimodel Ensemble. We demonstrate that forecasts which best simulate sea surface temperatures at the earliest lead times sustain advantages in forecast skill and find that correctly simulating extratropical atmospheric circulation increases the predictive skill of sea surface temperatures in the northeast Pacific in the following lead times. Our results offer North Pacific atmospheric circulation as a target for forecast model improvement that would additionally enhance ocean forecasts.

Analyses of sea surface chlorophyll a trends and variability from 1998 to 2020 in the German Bight (North Sea)

Abstract. Satellite remote sensing of ocean colour properties allows observation of the ocean with high temporal and spatial coverage, facilitating the better assessment of changes in marine primary production. Ocean productivity is often assessed using satellite-derived chlorophyll a concentrations, a commonly used proxy for phytoplankton concentration. We used the Copernicus GlobColour remote sensing chlorophyll a surface concentration to investigate seasonal and non-seasonal variability, temporal trends, and changes in spring bloom chlorophyll a magnitude. Complementary, we analysed the chlorophyll a relationship with sea surface temperature and mixed-layer depth in the German Bight from 1998 to 2020. Empirical orthogonal functions were employed in order to investigate dominant spatial and temporal patterns (modes) related to the main processes of chlorophyll a variability. Multi covariance analysis was used to extract the dominant structures that maximize the covariance between chlorophyll a and sea surface temperature mixed-layer depth fields. High levels of chlorophyll a were found near the coast, showing a decreasing gradient towards offshore waters. A significant chlorophyll a positive trend was observed close to the Elbe estuary and adjacent area, while 55 % of the German Bight was characterized by a significant chlorophyll a negative trend. The chlorophyll a non-seasonal variability showed that the first four modes explained around 45 %, with the first and second modes related to inter- and intra-annual variability, respectively, observed in the temporal principal components spectral analyses. Monthly chlorophyll a concentration anomalies co-varied by 45 % with sea surface temperature anomalies and 23 % with mixed-layer depth anomalies. The monthly averages of chlorophyll a anomaly fields were suitable to investigate long-term trends and variability. The rising water temperature, combined with its indirect effects on other variables, can partially explain the observed trends in chlorophyll a.

Development of Asymmetric Convection in a Tropical Cyclone under Environmental Uniform Flow and Vertical Wind Shear

AbstractIdealized numerical simulations have been carried out to reveal the complexity in the development of asymmetric convection in a tropical cyclone (TC) under the influence of an environment with either uniform flow, vertical wind shear (VWS), or both. Results show that rainwater is enhanced to the right of the motion in the outer rainband, but such enhancement occurs in the upshear-left area of the inner-core region. Additionally, due to the asymmetries introduced by environmental flow, wavenumber-1 temperature and height anomalies develop at a radius of ~1000 km in the upper levels. A sub-vortex aside from the TC center encompassing the wavenumber-1 warm center appears, and asymmetric horizontal winds emerge, which, in turn, changes the storm-scale (within 400 km) VWS. Deep convection in the inner core closely follows the changing storm-scale VWS when its magnitude is larger than 2 m s–1 and is located downshear of the storm-scale VWS in all the experiments with environmental flow. In the outer rainbands, the maximum boundary layer convergence is mainly controlled by the direction of motion and is located in the rear-right quadrant. These results extend upon the findings of previous studies in three aspects: (1) The discovery of the roughly linear combination effect from the uniform flow and large-scale VWS; (2) The development of upper-level asymmetric winds on a 1000-km scale through the interaction between the TC vortex and environmental flow, resulting in changes in the storm-scale VWS pattern within the TC area; (3) The revelation that TC asymmetric convection closely aligns with the direction-varying storm-scale VWS instead of the initially designated VWS.

Contrasting influence of the 1997 and 2015 El Niño on the Indian Summer Monsoon Rainfall: Role of the Southern Hemisphere

This study provides comprehensive analysis on the contrasting effects caused by the 1997 and 2015 historical El Niño events linked with the Indian Summer Monsoon (ISM) rainfall. The presence of strong southeast-northwest oriented cold Sea Surface Temperature (SST) anomalies during 1997, that spatially extended from southwest Pacific to Southeast Indian Ocean (SEIO) in comparison to the 2015 event is the robust feature. Evidently, these anomalies are closely related to interaction between cyclonic (anticyclonic) circulation over South Pacific Convergence Zone (south Australia). During 2015, the conjunction of Modoki II and classical El Niño triggered an asymmetrical equatorial circulation throughout the Indo-western Pacific (IWP) and thereby stimulated the Southern Annular Mode (SAM) through troposphere and stratospheric pathway mechanism. In addition, in 2015, SAM impacted the Indian Ocean, which intern affected ISM rainfall. Positive SAM associated with westward shift of anticyclone over south of Australia alters the circulation by inducing westerly winds over the South Indian Ocean, thereby suppressing Indian Ocean Dipole (IOD), and inducing drought conditions over India during 2015. Moreover, the AUS index, an indicator for IOD strength in boreal summer, is a bridging factor prevalent over mid-latitude regions in the southern hemisphere. Results from this study indicate the complex interaction of southern hemisphere atmospheric flow and its role in modulating the Indian Ocean region thereby ISM rainfall. A better understanding of these underlying mechanism can significantly enhance the predictive skills and projections of monsoon variability and extremes.