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The last 12 months have provided further evidence of the potential for cascading ecological and socio-political crises that were warned of 12 months ago. Then a consensus statement from the Regional Action on Climate Change Symposium warned: “the Earth’s climatic, ecological, and human systems are converging towards a crisis that threatens to engulf global civilization within the lifetimes of children now living.” Since then, the consequences of a broad set of extreme climate events (notably droughts, floods, and fires) have been compounded by interaction with impacts from multiple pandemics (including COVID-19 and cholera) and the Russia–Ukraine war. As a result, new connections are becoming visible between climate change and human health, large vulnerable populations are experiencing food crises, climate refugees are on the move, and the risks of water, food, and climate disruption have been visibly converging and compounding. Many vulnerable populations now face serious challenges to adapt. In light of these trends, this year, RACC identifies a range of measures to be taken at global and regional levels to bolster the resilience of these populations in the face of such emerging crises. In particular, at all scales, there is a need for globally available local data, reliable analytic techniques, community capacity to plan adaptation strategies, and the resources (scientific, technical, cultural, and economic) to implement them. To date, the rate of growth of the support for climate change resilience lags behind the rapid growth of cascading and converging risks. As an urgent message to COP27, it is proposed that the time is now right to devote much greater emphasis, global funding, and support to the increasing adaptation needs of vulnerable populations.

Sea surface temperature (SST) is a key physical attribute of upper ocean thermal conditions that provide crucial information on the earth’s climate system by playing vital role in air-sea interactions. Some regional-scale SST variations are linked to large-scale climate variability which has catastrophic consequences in the social-economic sectors of many countries. Such anomalous SST conditions in the tropical oceans causes severe impacts on the functioning of terrestrial ecosystems by altering the fluxes of heat and moisture on land, and thus threatens terrestrial carbon dynamics as well as global food security. Thus, monitoring the vegetation response to SST anomalies is fundamental to understand, quantify, and predict the effects of oceanic variability on terrestrial vegetation activity. Solar-induced chlorophyll fluorescence (SIF) is a promising plant biophysical variable that has been used for the continuous observation of global vegetation activity, especially the photosynthetic characteristics. Our study comprehensively evaluates the relationship between tropical Pacific SST variability and SIF anomalies across India to assess the spatial and temporal variability in the ocean-vegetation interactions. Overall, SIF anomaly over the Indian mainland shows negative association with SST variability in the eastern equatorial Pacific. The persistence of warm anomalies in this oceanic region forces the reduction of average SIF in all the Indian agro-climatic zones notably during the summer monsoon. While during the years of cold anomalies in the eastern equatorial Pacific, SIF appears to be enhanced. Similarly, the composite of SIF demonstrated negative (positive) anomalies during the years of positive (negative) SST anomalies. However, the implications of SST variability on the SIF anomalies are not uniform all over India even during the summer monsoon. There exist a high spatial and temporal variability in the observed SST-SIF interactions. Within the monsoon months, the influence of both positive and negative SST anomalies was predominant only during July and August across much of the Indian mainland. In addition, this oceanic influence was also significantly notable in March, particularly in the Deccan plateau. Overall, the impact of warm anomalies is comparatively stronger on the functioning of the terrestrial ecosystem in India than the cold anomalies with a limited influence mainly over the southern peninsular region. This difference in the implications of positive and negative SST anomalies is evident in all the months except during March, July, and August. Annually, SST variability in the eastern equatorial Pacific significantly contributes to the interannual variability of SIF anomalies in Gujarat plains and hills, Western plateau and hills, Southern plateau and hills, Central plateau and hills, Eastern plateau and hills, and Western dry region. The observed significant SST-SIF linkage between the eastern equatorial Pacific and the Indian vegetation was feasible through the atmospheric teleconnections. The present study provides the fundamental information that aids the early detection of possible vegetation growth anomalies to various climate extremes associated with the tropical Pacific region. This can be useful for planning long-term strategies and policies to improve precision agriculture and forest management practices in India.

Using empirical orthogonal function analysis, a stationary atmospheric wavenumber-4 (AW4) pattern is identified in the Southern mid-latitudes during austral summer. The generation mechanism and its linkage to the Southern Hemisphere climate are explored using a linear response model and composite analysis. It is found that AW4 pattern is forced by a Rossby wave source in the upstream region of the upper-tropospheric westerly waveguide. The vortex stretching associated with the anomalous convection over the subtropical western Pacific Ocean (near the New Zealand coast) adjacent to the westerly jet triggers the Rossby wave train around mid-November. This disturbance gets trapped in the Southern Hemisphere westerly jet waveguide and circumnavigates the globe. Around 15–25 days later (in early December), a steady AW4 pattern is established in the Southern mid-latitudes. Further, correlation analysis suggests the AW4 pattern is independent of other natural variabilities such as El Nino/Southern Oscillation, Southern Annular Mode, and Indian Ocean Dipole. The AW4 pattern is found to influence the rainfall over different parts of South America and Australia by modulating upper-level divergence.

We have been developing a dynamical seasonal prediction system based on a climate model called the Scale Interaction Experiment-Frontier ver. 2 (SINTEX-F2) under the EU–Japan collaborative framework. It has demonstrated high skills in the prediction of climate phenomena in the tropical Pacific and Indian Oceans (e.g., El Niño/Southern Oscillation (ENSO), ENSO-Modoki, and Indian Ocean Dipole events) and their teleconnections. Encouraged by the performance, we further explored the predictability of drought in East Africa during the short rains season of 2021. Many parts of East Africa experienced extremely dry conditions during the short rains season of October–December of this year. Interestingly, this was predicted a few months earlier by the 108-member ensemble seasonal prediction system based on the SINTEX-F climate model. Based on the co-variability of inter-member anomalies, we found that the 108-member ensemble prediction system has an advantage in finding possible co-variability patterns influencing predictions of precipitation, in which the signal-to-noise ratio is low relative to predictions of temperature. This analysis demonstrates that the 2021 negative Indian Ocean Dipole was responsible for the unusually dry conditions over East Africa. We also developed a hybrid statistical-dynamical framework that was found to be more skillful than the original SINTEX-F model at predicting drought in East Africa for a longer lead time. We hope this added predictability will help people take the necessary mitigation measures to reduce the devastating impact of the drought., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

The selective ensemble mean (SEM) technique is applied to the late spring and summer months (May–August) surface air temperature anomaly predictions of the Scale Interaction Experiment–Frontier Research Center for Global Change, version 2 (SINTEX-F2), coupled general circulation model over Japan. Using the Köppen–Geiger climatic classification we chose four regions over Japan for applying the SEM technique. The SINTEX-F2 ensemble members for the SEM are chosen based on the anomaly correlation coefficients (ACC) of the SINTEX-F2 predicted and observed surface air temperature anomalies. The SEM technique is applied to generate the forecasts of the surface air temperature anomalies for the period 1983–2018 using the selected members. Analysis shows the ACC skill score of the SEM prediction to be higher compared to the ACC skill score of predictions obtained by averaging all the 24 members of the SINTEX-F2 (ENSMEAN). The SEM predicted surface air temperature anomalies also have higher hit rate and lower false alarm rate compared to the ENSMEAN predicted anomalies over a range of temperature anomalies. The results indicate the SEM technique to be a simple and easy to apply method to improve the SINTEX-F2 predictions of surface air temperature anomalies over Japan. The better performance of the SEM in generating the surface air temperature anomalies can be partly attributed to realistic prediction of 850-hPa geopotential height anomalies over Japan.

The study examines the influence of external climate forcings, and atmosphere–ocean–sea–ice coupled interaction on the Southern Hemisphere (SH) atmospheric circulation variability. We analysed observed and simulated changes in view of Antarctic sea–ice and Southern Ocean surface temperature trends over recent decades. The experiment embraces both idealised and comprehensive methods that involves an Earth System Model (ESM) prototype. The sensitivity experiment is conducted in a manner that decomposes the signatures of sea–ice, sea surface temperature and feedback mechanisms. The results reveal that the Southern Annular Mode (SAM) multidecadal variability is found to be modulated by coupled interactions whereas its sub-seasonal to interannual vacillation seems to follow a random trajectory. The latter may strengthen the notion that its predictability is limited even with the use of ESMs. Most of the atmospheric circulation variability and recent changes may be explained by the ocean thermal forcing and coupled interactions. However, the influence of sea–ice forcing alone is largely indistinguishable and predominantly localised in nature. The result also confirms that the Antarctic dipole-like sea–ice pattern, a leading climate mode in the SH, has intensified in the last three decades irrespective of season. The probable indication is that processes within the Southern Ocean may play a key role, which deserves further investigation.

This is a maiden attempt to explore the influence of sea surface temperature (SST) variations in the tropical Indian Ocean on the gross primary productivity (GPP) of the terrestrial vegetation of India during the summer monsoon. We studied the productivity of the vegetation across India using solar-induced chlorophyll fluorescence (SIF) as a proxy. Our results demonstrated a strong negative SST–SIF relationship: the productivity decreases (increases) when the SST of the tropical Indian Ocean is higher (lower) than normal. This SST–SIF coupling observed during June can be explained through the atmospheric teleconnections. Positive SST anomalies weaken the land–ocean thermal gradient during the monsoon onset period, reduce the monsoon flow, and hence decrease the moisture transport from the ocean to the Indian mainland. The resultant water stress, along with the high air temperature, leads to a reduction in the GPP. Conversely, negative SST anomalies strengthen the monsoon and increase the availability of moisture for photosynthesis. There is scope for improving regional GPP forecasting studies using the observed SST–SIF relationships.

The out of phase tropical cyclone (TC) formation in the subtropical and tropical western North Pacific associated with local low-level wind vorticity anomaly, driven by the remote central and eastern equatorial Pacific warming/cooling, is investigated based on the reanalysis and observational data in the period of 1979−2017. TC frequencies in the subtropical and tropical western North Pacific appear to be connected to different remote heating/cooling sources and are linked to eastern and central Pacific warming/cooling, which are in turn related to canonical El Niño/Southern Oscillation (ENSO) and ENSO Modoki, respectively. TCs formed in subtropics (SfTC) are generally found to be associated with a dipole in wind vorticity anomaly, which is driven by the tropical eastern Pacific warming/cooling. Tropically formed TCs (TfTC) are seen to be triggered by the single-core of wind vorticity anomaly locally associated with the warming/cooling of central and eastern Pacific. The predicted ENSOs and ENSO Modokis, therefore, provide a potential source of seasonal predictability for SfTC and TfTC frequencies.

The machine learning technique, namely Artificial Neural Networks (ANN), is used to predict the surface air temperature (SAT) anomalies over Japan in the winter months of December, January and February for the period 1949/50 to 2019/20. The predictions are made for the four regions Hokkaido, North, Central and West of Japan. The inputs to the ANN model are derived from the anomaly correlation coefficients among the SAT anomalies over the regions of Japan and the global SAT and sea surface temperature anomalies. The results are validated using anomaly correlation coefficient (ACC) skill scores with the observation. It is found that the ANN predictions over Hokkaido have higher ACC skill scores compared to the ACC scores over the other three regions. The ANN predicted SAT anomalies are compared with that of ensemble mean of 8 of the North American Multi-Model Ensemble (NMME) models besides comparing them with the persistent anomalies. The ANN predictions over all the four regions have higher ACC skill scores compared to the NMME model skill scores in the common period of 1982/83 to 2018/19. The ANN predicted SAT anomalies also have higher Hit rate and lower False alarm rate compared to the NMME predicted SAT anomalies. All these indicate that the ANN model is a promising tool for predicting the winter SAT anomalies over Japan.

Skillful sea-ice prediction in the Antarctic Ocean remains a big challenge due to paucity of sea-ice observations and insufficient representation of sea-ice processes in climate models. This study demonstrates that the Antarctic sea-ice concentration (SIC) prediction is significantly improved using a coupled general circulation model (SINTEX-F2) in which the model’s SIC and sea-ice thickness (SIT) are initialized with the ocean/sea-ice reanalysis product (C-GLORSv7). It is found that the wintertime SIT initialization adds positive values to the prediction skills of the summertime SIC, most effectively in the Weddell Sea where the SIT climatology and variability are the largest among the Antarctic Seas. Examination of the SIT balance during low sea-ice years of the Weddell Sea shows that negative SIT anomalies initialized in June retain the memory throughout austral winter (July-September) owing to horizontal advection of the SIT anomalies by sea-ice velocities. The negative SIT anomalies continue to develop in austral spring (October-December) owing to more incoming solar radiation via ice-albedo feedback and the associated warming of mixed layer. This results in further sea-ice decrease during austral summer (January-March). Concomitantly, the model reasonably reproduces atmospheric circulation anomalies in the Amundsen-Bellingshausen Seas as well as the Weddell Sea during the development of the negative sea-ice anomalies. These results provide solid evidence that the wintertime SIT initialization benefits skillful summertime sea-ice prediction in the Antarctic Seas.

Presence of a stationary zonal wavenumber-4 (W4) pattern is revealed in the sea surface temperature (SST) anomaly over southern subtropics (20°S-55°S) using empirical orthogonal function analysis. This W4 pattern is found to be seasonally phase-locked to the austral summer (persists up to mid-autumn) and independent of other known tropical and extra-tropical climate phenomena. A thermodynamic coupling of atmosphere and the upper ocean helps in generating the W4 pattern, which later terminates due to the breaking of the ocean-atmosphere positive feedback. Due to anomalous convection over western subtropical pacific near the westerly jet, the signal appears first in the atmosphere during early November. Later, the disturbance gets trapped in the westerly waveguide which circumnavigates the globe and produces an atmospheric W4 pattern in early December (20-30 days later). Then, the signal transported to the ocean through the ocean-atmosphere feedback and sustained in the ocean (after it disappears from the atmosphere) as it has high specific heat capacity. During the positive phase of the W4 event, the cold SST anomaly develops over the southeastern and -western side (SE-NW) of Australia creating an anomalous divergence circulation. It favours the moisture transport towards the south-eastern region of the continent. Consequently, the specific humidity increases and causes an above-normal rainfall in a SE-NW axis over Australia. An opposite process is seen in case of a negative W4 event.

Atmosphere and oceans are the major components of the Earth’s climate system and play important roles in determining the intra-seasonal to inter-decadal climate variations discussed in this book. This introductory chapter broadly discusses the internal physical and dynamical processes in both systems in addition to the air–sea interactions that form the basis for basin-scale climate variations such as the El Nino/Southern Oscillation and the Indian Ocean Dipole discussed in the subsequent chapters. The variations in coastal processes are also important for the regional marine ecosystems and regional climate variations. These are also briefly introduced here and covered in some of the chapters of this book.

International audience; The Indian Ocean Dipole (IOD) is one of the dominant modes of variability of the tropical Indian Ocean and it has been suggested to have a crucial role in the teleconnection between the Indian summer monsoon and El Niño Southern Oscillation (ENSO). The main ideas at the base of the influence of the IOD on the ENSO-monsoon teleconnection include the possibility that it may strengthen summer rainfall over India, as well as the opposite, and also that it may produce a remote forcing on ENSO itself. In the future, the IOD is projected to increase in frequency and amplitude with mean conditions mimicking the characteristics of its positive phase. Still, state-of-the-art global climate models have large biases in representing mean state and variability of both IOD and ISM, with potential consequences for their future projections. However, the characteristics of the IOD and ENSO are likely to continue in a future warmer world, with a persistence of their linkage.

The El Nino/Southern Oscillation (ENSO) is one of the dominant modes of climate variations with widespread global impacts. The ENSO research has come a long way from its early days in the 1980s when the event of 1982 was not even recognized until its late developments. Since then, rapid progresses are made in the monitoring, data analyses, theory, and modeling to improve our understanding of the phenomenon and its prediction. Several mechanisms are proposed to explain the ENSO cycle and its seasonal to decadal variability. It appears that a combination of all those proposed mechanisms that could explain the reality and the nonlinearity inherent in the system is affected by nonlinear interactions among several processes not only in the tropical Pacific but also in the neighboring basins. This creates a challenge for developing skillful predictions of the phenomenon at long lead times. Nevertheless, the present generations of climate models are able to predict it at least a couple of seasons ahead. Some of the models, like the Scale Interaction Experiment-Frontier Research Center for Global change Version 1 model, are able to predict it even 15–18 months ahead. The changes in the mean state owing to global warming have brought changes in the ENSO behavior. More numbers of ENSO Modoki events are seen in recent decades. Also, a change in the tropical Pacific Walker circulation aided by the warming in the Indian and Atlantic Oceans has helped in developing a La Nina-like state since 2000. How the ENSO will behave in a changed climate remains quite uncertain owing to lack of long observations and uncertainties in model-based projections.

Interannual-to-decadal sea surface temperature (SST) variability in South Atlantic and southern Indian Oceans has substantial influence on austral summer rainfall over southern Africa through modulation of atmospheric circulation. This chapter provides a review of previous findings on physical processes and predictability of the interannual SST variability, so-called “subtropical dipole,” which is characterized by a meridional dipole pattern of SST anomalies in each basin. In addition, a recently identified decadal SST variability, which shows distinct eastward propagation of SST anomalies along the Antarctic Circumpolar Current from the South Atlantic to the southern Indian Ocean, is briefly described.

The El Nino/Southern Oscillation (ENSO) has been recognized as the dominant mode of climate variations with widespread global impacts. However, ENSO variability and its teleconnections are influenced by other modes of climate variations. The Indian Ocean Dipole (IOD) is one such climate mode. For example, the IOD has changed the way ENSO influences the Indian summer monsoon rainfall (ISMR). It is found that the adverse effect of El Ninos on the ISMR has diminished a lot during recent decades when the positive IODs (pIODs) evolved more frequently. However, the frequent occurrences of pIODs have been very unfavorable for the eastern sphere of the IOD. Many parts of Australia and Indonesia report frequent droughts and forest fires during these pIOD years. Therefore the predictability of the IOD at long-lead times is sought from the operational centers to develop mitigation measures. While most models, especially the Scale Interaction Experiment-Frontier Research Center for Global Change prediction system, have shown skills to predict the IOD, it remains a challenge to predict them at sufficiently long-lead times. Nevertheless, there are some exceptions like the 2019 pIOD event. This event was unique in the sense that it was a record-breaking pIOD and was observed in absence of El Nino. This chapter discusses this unique event at length and reviews past studies in the context of this exceptional phenomenon.

Exploratory analysis using empirical orthogonal function revealed the presence of a stationary zonal wavenumber-4 (W4) pattern in the sea surface temperature (SST) anomaly in the southern subtropics (20°S–55°S). The signal over the Southern subtropics is seasonally phase-locked to the austral summer and persists up to mid-autumn. Thermodynamic coupling of atmosphere and the upper ocean helps in generating the W4 pattern, which later terminates due to the breaking of that coupled feedback. It is found that the presence of anomalous SST due to W4 mode in the surrounding of Australia affects the rainfall over the continent by modulating the local atmospheric circulation. During positive phase of W4 event, the presence of cold SST anomaly over the south-eastern and -western side of Australia creates an anomalous divergence circulation. This favours the moisture transport towards south-eastern Australia, resulting in more rainfall in February. The scenario reverses in case of a negative W4 event. There is also a difference of one month between the occurrence of positive and negative W4 peaks. This asymmetry seems to be responsible for the weak SST signal to the South of Australia. Correlation analysis suggests that the W4 pattern in SST is independent of other natural variabilities such as Southern Annular Mode, and Indian Ocean Dipole as well as a rather weak relationship with El Niño/Southern Oscillation.

In this paper, the rainfall trend of the West Coast Plain and Hill Agro-Climatic Region is analyzed for 117 years (1901&ndash, 2017). This region is a globally recognized biodiversity hotspot and known for one of the highest rainfall receiving regions in India. Rainfall grid dataset is used for the analysis of rainfall trends on monthly, seasonal, and decadal time scales. Modified Mann&ndash, Kendall&rsquo, s test, Linear Regression, Innovative Trend Analysis, Sen&rsquo, s Slope test, Weibull&rsquo, s Recurrence Interval, Pearson&rsquo, s Coefficient of Skewness, Consecutive Disparity Index, Kurtosis, and some other important statistical techniques are employed for trend analysis. Results indicate that the rainfall trend is significant in January, July, August, September as well as the Winter season. Among all the significant trends, January and July showed a decreasing rainfall trend. July has the highest contribution (30%) among all the obtained monotonic trend to annual rainfall and coincidentally has the highest trend magnitude. August and September months with a combined contribution of 30% to annual rainfall, show an increasing monotonic trend with high magnitude whereas Winter season shows a monotonic decreasing rainfall trend with comparatively low magnitudes. Decadal analysis along with the study of recurrence interval of excess and deficit years helps to understand the decadal rhythm of trend and the magnitude of extreme monthly and seasonal events. Skewness reveals that rainfall dataset of all the periodic results is right-skewed and the recurrence interval also supports the skewness results. Sharply decreasing rainfall in July and rising rainfall in August and September is predictive of the impact on agriculture, biodiversity and indicates the rainfall regime shift in the region.

When COVID-19 began to spread, environmental scientists recognized that the world faced a dangerous upper respiratory viral disease that might exhibit sensitivity to seasonal weather conditions. Many of these scientists have sought to aid COVID-19 response by studying the potential to monitor, forecast, or project disease transmission rates or symptom severity as a function of climate zone, season, meteorological variability, air quality, and other environmental parameters1,2,3,4. The rapid pace of COVID-19 research has meant that studies on this topic appeared on pre-print servers and then on news and social media outlets faster than the information could be cross-checked and peer-reviewed. As many such studies accumulated, it became clear that reported evidence was often contradictory, and in some cases studies were being selected subjectively in a manner that seemed intended to support political agendas. Carlson et al.5 recently provided a cogent assessment of the policy-relevant challenges associated with studies that have attempted to quantify meteorological sensitivities of the virus and the disease. We appreciate this perspective. Here we argue that the research community must act to ensure that work on this topic meets its potential to contribute to pandemic understanding and response, and that fears of inappropriate data analysis or miscommunication do not dampen innovation or the effective use of research results. Por pares

Extreme sea level rise seriously impacts habitation and is indicative of changes in primary production in the North Pacific. Because of its rising trend associated with global warming, skillful seasonal-to-interannual predictions have become increasingly valuable to guide the introduction of suitable adaptation measures that help us reduce the risks of socio-economic losses. Here, we have used a dynamical coupled ocean–atmosphere model called “SINTEX-F” to revisit the potential predictability of sea level anomalies at a lead of up to about two years. Skillful prediction is found mainly in the tropical Pacific as shown in previous work. Here, we found a new skillful prediction region in the North Pacific (30o-40oN, 180oW-150oW) at about two years lead time. We also analyzed the co-variability among ensemble members and found the long-lasting ENSO/ENSO-Modoki in the tropical Pacific seems to contribute to the predictability source. The result may be useful to develop systematic and synergistic attempts to predict marine ecosystem responses to regional and global climate variations.

The impact of Indo-Pacific climate variability in the South Asian region is very pronounced and their impact on agriculture is very important for the Indian subcontinent. In this study, rice productivity, climatic factors (Rainfall, Temperature and Soil Moisture) and associated major Indo-Pacific climate indices in Bihar were investigated. Bihar is one of the major rice-producing states of India and the role of climate variability and prevailing climate indices in six events (between 1991&ndash, 2014) with severer than &minus, 10% rice productivity are analyzed. The Five-year moving average, Pearson&rsquo, s Product Moment Correlation, Partial Correlation, Linear Regression Model, Mann Kendall Test, Sen&rsquo, s Slope and some other important statistical techniques were used to understand the association between climatic variables and rice productivity. Pearson&rsquo, s Product Moment Correlation provided an overview of the significant correlation between climate indices and rice productivity. Whereas, Partial Correlation provided the most refined results on it and among all the climate indices, Ni&ntilde, o 3, Ocean Ni&ntilde, o Index and Southern Oscillation Index are found highly associated with years having severer than &minus, 10% decline in rice productivity. Rainfall, temperature and soil moisture anomalies are analyzed to observe the importance of climate factors in rice productivity. Along with the lack of rainfall, lack of soil moisture and persistent above normal temperature (especially maximum temperature) are found to be the important factors in cases of severe loss in rice productivity. Observation of the dynamics of ocean-atmosphere coupling through the composite map shows the Pacific warming signals during the event years. The analysis revealed a negative (positive) correlation of rice productivity with the Ni&ntilde, o 3 and Ocean Ni&ntilde, o Index (Southern Oscillation Index).