Tropical ocean teleconnections with gross primary productivity of monsoon-Asia.

The intricate oceanic climate interactions with terrestrial primary production of Asian ecosystems exert crucial social-economical-environmental repercussions. Yet, a holistic understanding of tropical sea surface temperature (SST) anomalies associated with the gross primary productivity (GPP) variations of monsoon-Asia remains constrained. This study provides a statistical framework demonstrating how SST perturbations in the tropics influence GPP fluctuations in monsoon-Asia by modulating hydrothermal conditions of different climate system components. Observation evidence explicitly illustrated the characteristic anomalous SST signatures of positive and negative GPP anomalies in South and Southeast Asia during June-August. The SST anomalies of the central-eastern tropical Pacific showed a robust negative impact on the GPP variability of South-Asia. The GPP alterations in maritime-Southeast-Asia exhibited strong connections with SST anomalies of the western Pacific (positive) and eastern equatorial Pacific (negative). The oceanic signals in the GPP variability of South-Asia and maritime-Southeast-Asia mirrored canonical El Niño and La Niña patterns. The detected SST-GPP link is feasible through large-scale atmospheric circulation variability and the consequent regional modulation of heat and moisture fluxes. The anomalous strengthening (weakening) of Walker cell enhance (reduce) water available to plants for photosynthesis during La Niña (El Niño) phase of ENSO cycle and thus elevate (lower) GPP in South-Asia and Maritime-southeast-Asia. In contrast, the enhanced GPP anomaly in mainland-Southeast-Asia depicts signs of canonical La Niña and Indian Ocean subtropical dipole (IOSD) teleconnections. The positive impact of IOSD was through the modulation of the Mascarene High and the consequent impact on monsoon. Meanwhile, decreased GPP bears the imprint of El Niño Modoki and warm tropical Indian Ocean SSTs. The atmospheric teleconnections demonstrated the delayed impact of El Niño Modoki on GPP variability through the Indian Ocean capacitor effect. Our findings could be instrumental in forecasting the probable effects on vegetation growth in monsoon-Asia associated with high-frequency tropical oceanic changes.

Artificial intelligence predicts normal summer monsoon rainfall for India in 2023.

Inaccuracy in the All Indian Summer Monsoon Rainfall (AISMR) forecast has major repercussions for India's economy and people's daily lives. Improving the accuracy of AISMR forecasts remains a challenge. An attempt is made here to address this problem by taking advantage of recent advances in machine learning techniques. The data-driven models trained with historical AISMR data, the Niño3.4 index, and categorical Indian Ocean Dipole values outperform the traditional physical models, and the best-performing model predicts that the 2023 AISMR will be roughly 790 mm, which is typical of a normal monsoon year.

Utilizing a novel high-resolution malaria dataset for climate-informed predictions with a deep learning transformer model.

Climatic factors influence malaria transmission via the effect on the Anopheles vector and Plasmodium parasite. Modelling and understanding the complex effects that climate has on malaria incidence can enable important early warning capabilities. Deep learning applications across fields are proving valuable, however the field of epidemiological forecasting is still in its infancy with a lack of applied deep learning studies for malaria in southern Africa which leverage quality datasets. Using a novel high resolution malaria incidence dataset containing 23 years of daily data from 1998 to 2021, a statistical model and XGBOOST machine learning model were compared to a deep learning Transformer model by assessing the accuracy of their numerical predictions. A novel loss function, used to account for the variable nature of the data yielded performance around + 20% compared to the standard MSE loss. When numerical predictions were converted to alert thresholds to mimic use in a real-world setting, the Transformer's performance of 80% according to AUROC was 20-40% higher than the statistical and XGBOOST models and it had the highest overall accuracy of 98%. The Transformer performed consistently with increased accuracy as more climate variables were used, indicating further potential for this prediction framework to predict malaria incidence at a daily level using climate data for southern Africa.

Predicting maximum temperatures over India 10-days ahead using machine learning models.

In the months of March-June, India experiences high daytime temperatures (Tmax), which sometimes lead to heatwave-like conditions over India. In this study, 10 different machine learning models are evaluated for their ability to predict the daily Tmax anomalies 10 days ahead in the months of March-June. Several model experiments were carried out to identify an optimal model to predict daily Tmax anomalies over India. The results indicate that the AdaBoost regressor with Multi-layer Perceptron as the base estimator is an optimal model to predict the Tmax anomalies over India in the months of March-June. The optimal model predictions are benchmarked against 10-day persistence predictions and the predictions from the Climate Forecast System (CFS) reforecast. The results indicate that the machine learning model skill is higher than persistence and comparable to CFS reforecast 10-day predictions in April and May. In March and June, the machine learning models have low skill scores and perform no better than persistence. These results indicate that the machine learning models are promising tools to predict the surface air maximum temperature anomalies over India in April and May and can complement predictions from more sophisticated numerical models.

An urgent need for COP27: confronting converging crises.

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.

Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning.

Malaria is the cause of nearly half a million deaths worldwide each year, posing a great socioeconomic burden. Despite recent progress in understanding the influence of climate on malaria infection rates, climatic sources of predictability remain poorly understood and underexploited. Local weather variability alone provides predictive power at short lead times of 1-2 months, too short to adequately plan intervention measures. Here, we show that tropical climatic variability and associated sea surface temperature over the Pacific and Indian Oceans are valuable for predicting malaria in Limpopo, South Africa, up to three seasons ahead. Climatic precursors of malaria outbreaks are first identified via lag-regression analysis of climate data obtained from reanalysis and observational datasets with respect to the monthly malaria case count data provided from 1998-2020 by the Malaria Institute in Tzaneen, South Africa. Out of 11 sea surface temperature sectors analyzed, two regions, the Indian Ocean and western Pacific Ocean regions, emerge as the most robust precursors. The predictive value of these precursors is demonstrated by training a suite of machine-learning classification models to predict whether malaria case counts are above or below the median historical levels and assessing their skills in providing early warning predictions of malaria incidence with lead times ranging from 1 month to a year. Through the development of this prediction system, we find that past information about SST over the western Pacific Ocean offers impressive prediction skills (~80% accuracy) for up to three seasons (9 months) ahead. SST variability over the tropical Indian Ocean is also found to provide good skills up to two seasons (6 months) ahead. This outcome represents an extension of the effective prediction lead time by about one to two seasons compared to previous prediction systems that were more computationally costly compared to the machine learning techniques used in the current study. It also demonstrates the value of climatic information and the prediction framework developed herein for the early planning of interventions against malaria outbreaks.

Climate Precursors of Satellite Water Marker Index for Spring Cholera Outbreak in Northern Bay of Bengal Coastal Regions.

Cholera is a water-borne infectious disease that affects 1.3 to 4 million people, with 21,000 to 143,000 reported fatalities each year worldwide. Outbreaks are devastating to affected communities and their prospects for development. The key to support preparedness and public health response is the ability to forecast cholera outbreaks with sufficient lead time. How Vibrio cholerae survives in the environment outside a human host is an important route of disease transmission. Thus, identifying the environmental and climate drivers of these pathogens is highly desirable. Here, we elucidate for the first time a mechanistic link between climate variability and cholera (Satellite Water Marker; SWM) index in the Bengal Delta, which allows us to predict cholera outbreaks up to two seasons earlier. High values of the SWM index in fall were associated with above-normal summer monsoon rainfalls over northern India. In turn, these correlated with the La Niña climate pattern that was traced back to the summer monsoon and previous spring seasons. We present a new multi-linear regression model that can explain 50% of the SWM variability over the Bengal Delta based on the relationship with climatic indices of the El Niño Southern Oscillation, Indian Ocean Dipole, and summer monsoon rainfall during the decades 1997-2016. Interestingly, we further found that these relationships were non-stationary over the multi-decadal period 1948-2018. These results bear novel implications for developing outbreak-risk forecasts, demonstrating a crucial need to account for multi-decadal variations in climate interactions and underscoring to better understand how the south Asian summer monsoon responds to climate variability.

Summertime sea-ice prediction in the Weddell Sea improved by sea-ice thickness initialization.

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. Using a coupled general circulation model, this study demonstrates skillful prediction of the summertime sea-ice concentration (SIC) in the Weddell Sea with wintertime SIC and sea-ice thickness (SIT) initializations. During low sea-ice years of the Weddell Sea, negative SIT anomalies initialized in June retain the memory throughout austral winter owing to horizontal advection of the SIT anomalies. The SIT anomalies continue to develop in austral spring owing to more incoming solar radiation and the associated warming of mixed layer, contributing to further sea-ice decrease during late austral summer-early autumn. Concomitantly, the model reasonably reproduces atmospheric circulation anomalies during austral spring in the Amundsen-Bellingshausen Seas besides the Weddell Sea. These results provide evidence that the wintertime SIT initialization benefits skillful summertime sea-ice prediction in the Antarctic Seas.

Global wave number-4 pattern in the southern subtropical sea surface temperature.

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.

Why apple orchards are shifting to the higher altitudes of the Himalayas?

Apple cultivation is one of the most important sources of livelihood in Indian side of the Himalayas. The present study focuses on the apple orchards of Himachal Pradesh, a state within the Himalayan Mountains, a major apple producers of India. In the study, it is found that the optimum apple growing conditions in the region have been consistently shifting and farmers are shifting their orchards to the higher altitudes. For example, orchards have shifted to 1500-2500 meters in the 2000s compared to the cultivated elevation of 1200-1500 meters during 1980s. As of 2014, apples are being cultivated at an elevation of more than 3500 meters, for example, the newly developed orchards of Leo village in upper Kinnaur and Keylong area of Lahul and Spiti districts. Chilling hours for different districts are calculated. The trend of temperature during the growth period, winter session and annual rainfall have been analysed using Mann-Kendall and Sen's slope test. Data catalogued from different time periods indicates that the northward shift (towards higher altitude) is due to changes in chilling hours, total annual rainfall and mean surface temperature during the apple growing season. The mean surface temperature in all the districts has increased by almost 0.5°C during last 2000-2014. These changes are directly related to global warming. While the changing climate is reducing the apple production in low altitudinal regions of the state, it is creating new opportunities for apple cultivation in higher altitudes as conditions are getting more favourable for apple growth in those higher regions. The associated socio-economic changes are posing new societal issues for the local farmers.

Publisher Correction: Malaria predictions based on seasonal climate forecasts in South Africa: A time series distributed lag nonlinear model.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Long-lead Prediction of ENSO Modoki Index using Machine Learning algorithms.

The focus of this study is to evaluate the efficacy of Machine Learning (ML) algorithms in the long-lead prediction of El Niño (La Niña) Modoki (ENSO Modoki) index (EMI). We evaluated two widely used non-linear ML algorithms namely Support Vector Regression (SVR) and Random Forest (RF) to forecast the EMI at various lead times, viz. 6, 12, 18 and 24 months. The predictors for the EMI are identified using Kendall's tau correlation coefficient between the monthly EMI index and the monthly anomalies of the slowly varying climate variables such as sea surface temperature (SST), sea surface height (SSH) and soil moisture content (SMC). The importance of each of the predictors is evaluated using the Supervised Principal Component Analysis (SPCA). The results indicate both SVR and RF to be capable of forecasting the phase of the EMI realistically at both 6-months and 12-months lead times though the amplitude of the EMI is underestimated for the strong events. The analysis also indicates the SVR to perform better than the RF method in forecasting the EMI.

A machine learning based prediction system for the Indian Ocean Dipole.

The Indian Ocean Dipole (IOD) is a mode of climate variability observed in the Indian Ocean sea surface temperature anomalies with one pole off Sumatra and the other pole near East Africa. An IOD event starts sometime in May-June, peaks in September-October and ends in November. Through atmospheric teleconnections, it affects the climate of many parts of the world, especially that of East Africa, Australia, India, Japan, and Europe. Owing to its large impacts, previous studies have addressed the predictability of the IOD using state of the art coupled climate models. Here, for the first-time, we predict the IOD using machine learning techniques, in particular artificial neural networks (ANNs). The IOD forecasts are generated for May to November from February-April conditions. The attributes for the ANNs are derived from sea surface temperature, 850 hPa and 200 hPa geopotential height anomalies, using a correlation analysis for the period 1949-2018. An ensemble of ANN forecasts is generated using 500 samples with replacement using jackknife approach. The ensemble mean of the IOD forecasts indicates the machine learning based ANN models to be capable of forecasting the IOD index well in advance with excellent skills. The forecast skills are much superior to the skills obtained from the persistence forecasts that one would guess from the observed data. The ANN models also perform far better than the models of the North American Multi-Model Ensemble (NMME) with higher correlation coefficients and lower root mean square errors (RMSE) for all the target months of May-November.

Role of climate variability in the potential predictability of tropical cyclone formation in tropical and subtropical western North Pacific Ocean.

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.

Malaria predictions based on seasonal climate forecasts in South Africa: A time series distributed lag nonlinear model.

Although there have been enormous demands and efforts to develop an early warning system for malaria, no sustainable system has remained. Well-organized malaria surveillance and high-quality climate forecasts are required to sustain a malaria early warning system in conjunction with an effective malaria prediction model. We aimed to develop a weather-based malaria prediction model using a weekly time-series data including temperature, precipitation, and malaria cases from 1998 to 2015 in Vhembe, Limpopo, South Africa and apply it to seasonal climate forecasts. The malaria prediction model performed well for short-term predictions (correlation coefficient, r > 0.8 for 1- and 2-week ahead forecasts). The prediction accuracy decreased as the lead time increased but retained fairly good performance (r > 0.7) up to the 16-week ahead prediction. The demonstration of the malaria prediction process based on the seasonal climate forecasts showed the short-term predictions coincided closely with the observed malaria cases. The weather-based malaria prediction model we developed could be applicable in practice together with skillful seasonal climate forecasts and existing malaria surveillance data. Establishing an automated operating system based on real-time data inputs will be beneficial for the malaria early warning system, and can be an instructive example for other malaria-endemic areas.

Improving seasonal forecasts of air temperature using a genetic algorithm.

Seasonal forecasts of air-temperature generated by numerical models provide guidance to the planners and to the society as a whole. However, generating accurate seasonal forecasts is challenging mainly due to the stochastic nature of the atmospheric internal variability. Therefore, an array of ensemble members is often used to capture the prediction signals. With large spread in the prediction plumes, it becomes important to employ techniques to reduce the effects of unrealistic members. One such technique is to create a weighted average of the ensemble members of seasonal forecasts. In this study, we applied a machine learning technique, viz. a genetic algorithm, to derive optimum weights for the 24-ensemble members of the coupled general circulation model; the Scale Interaction Experiment-Frontier research center for global change version 2 (SINTEX-F2) boreal summer forecasts. Our analysis showed the technique to have significantly improved the 2m-air temperature anomalies over several regions of South America, North America, Australia and Russia compared to the unweighted ensemble mean. The spatial distribution of air temperature anomalies is improved by the GA technique leading to better representation of anomalies in the predictions. Hence, machine learning techniques could help in improving the regional air temperature forecasts over the mid- and high-latitude regions where the model skills are relatively modest.

Role of sea-ice initialization in climate predictability over the Weddell Sea.

Potential impact of sea-ice initialization on the interannual climate predictability over the Weddell Sea is investigated using a coupled general circulation model. Climate variability in the Weddell Sea is generally believed to have association with remote forcing such as El Niño-Southern Oscillation and the Southern Annual Mode. However, sea-ice variability in the Weddell Sea has been recently suggested to play additional roles in modulating local atmospheric variability through changes in surface air temperature and near-surface baroclinicity. Reforecast experiments from September 1st, in which the model's sea-surface temperature (SST) and sea-ice concentration (SIC) are initialized with observations using nudging schemes, show improvements in predicting the observed SIC anomalies in the Weddell Sea up to four months ahead, compared to the other experiments in which only the model's SST is initialized. During austral spring (Oct-Dec) of lower-than-normal sea-ice years in the Weddell Sea, reforecast experiments with the SST and SIC initializations reasonably predict high surface air temperature anomalies in the Weddell Sea and high sea-level pressure anomalies over the Atlantic sector of the Southern Ocean. These results suggest that accurate initialization of sea-ice conditions during austral winter is necessary for skillful prediction of climate variability over the Weddell Sea during austral spring.

Philippines-Taiwan Oscillations and its connection to tropical cyclone frequency in the western North Pacific Ocean.

An atmospheric component of the Philippine-Taiwan Oscillations (PTOa) is used to examine its potential connection with tropical cyclone (TC) frequency in the western North Pacific in the period of 1979-2014. During positive PTOa years, more TCs are observed in the tropical western Pacific south of 18 °N where cyclonic wind anomaly appears. On the other hand, anti-cyclonic wind anomaly appears in the subtropics north of 18 °N and is associated with less TC formation there. The opposite wind vorticities in tropics/subtropics and associated variability in TC frequency reverse in negative PTOa phase. Besides, the negative contribution of ocean heat content suggests the relative importance of wind vorticity according to the oceanic cyclone genesis potential index. The PTOa provides a more direct explanation of the TC activity compared to other remotely linked phenomena at least for the past 36 years (1979-2014). Therefore, PTOa can potentially serve as a promising index for diagnosing or forecasting TC activity in the western North Pacific Ocean.