Global Climate Indices Research Articles

Global patterns of disaster and climate risk\u2014an analysis of the consistency of leading index-based assessments and their results

Indices assessing country-level climate and disaster risk at the global scale have experienced a steep rise in popularity both in science and international climate policy. A number of widely cited products have been developed and published over the recent years, argued to contribute critical knowledge for prioritizing action and funding. However, it remains unclear how their results compare, and how consistent their findings are on country-level risk, exposure, vulnerability and lack of coping, as well as adaptive capacity. This paper analyses and compares the design, data, and results of four of the leading global climate and disaster risk indices: The World Risk Index, the INFORM Risk Index, ND-GAIN Index, and the Climate Risk Index. Our analysis clearly shows that there is considerable degree of cross-index variation regarding countries’ risk levels and comparative ranks. At the same time, there is above-average agreement for high-risk countries. In terms of risk sub-components, there is surprisingly little agreement in the results on hazard exposure, while strong inter-index correlations can be observed when ranking countries according to their socio-economic vulnerability and lack of coping as well as adaptive capacity. Vulnerability and capacity hotspots can hence be identified more robustly than risk and exposure hotspots. Our findings speak both to the potential as well as limitations of index-based approaches. They show that a solid understanding of index-based assessment tools, and their conceptual and methodological underpinnings, is necessary to navigate them properly and interpret as well as use their results in triangulation.

Multi-scale investigation on streamflow temporal variability and its connection to global climate indices for unregulated rivers in India

AbstractWith the increasing stress on water resources for a developing country like India, it is pertinent to understand the dominant streamflow patterns for effective planning and management activities. This study investigates the spatiotemporal characterization of streamflow of six unregulated catchments in India. Firstly, Mann Kendall (MK) and Changepoint analysis were carried out to detect the presence of trends and any abrupt changes in hydroclimatic variables in the chosen streamflows. To unravel the relationships between the temporal variability of streamflow and its association with precipitation and global climate indices, namely, Niño 3.4, IOD, PDO, and NAO, continuous wavelet transform is used. Cross-wavelet transform and wavelet coherence analysis were also used to capture the coherent and phase relationships between streamflow and climate indices. The continuous wavelet transforms of streamflow data revealed that intra-annual (0.5 years), annual (1 year), and inter-annual (2–4 year) oscillations are statistically significant. Furthermore, a better understanding of the in-phase relationship between the streamflow and precipitation at intra-annual and annual time scales were well-captured using wavelet coherence analysis compared to cross wavelet transform. Furthermore, our analysis also revealed that streamflow observed an in-phase relationship with IOD and NAO, whereas there was a lag correlation with Niño 3.4 and PDO indices at intra-annual, annual and interannual time scales.

Spatial variability of south Asian summer monsoon extreme rainfall events and their association with global climate indices

The study explores the spatial variability and related characteristics of south Asian summer monsoon rainfall and extreme rainfall events based on APHRODITE rainfall products. An attempt was also made to investigate their association with global climate indices. Most of the South Asian regions are vulnerable to extreme rainfall events and many of these lead to major hydrological disasters. Such events are adversely affecting flora and fauna, causing large-scale damage to the livelihood and economy. We have analysed the characteristics of extreme rainfall events during the summer monsoon season for 65 years (1951–2015). The statistical method based on the percentile of daily rainfall amount (>99th percentile) was used to identify the extreme rainfall zones. We have also attempted to explore the spatio-temporal features and heterogeneity of these extreme events at a finer spatial resolution. We examined the linear trend of these extreme rain events on the spatial domain and found that extreme rainfall decreases over northcentral Indian and southwestern coastal belts. An increase in the frequency and amount of rainfall from extreme rain events were observed in the western coastal belts between 16° to 20° N and central Indian regions along with the coastal areas of Bangladesh and Myanmar. The linear trends of extremes exhibit remarkable differences from the trends of seasonal rainfall. The results from the probability density function (PDF) analysis are also supporting the trend analysis of mean and extremes in all the five regions. It was also observed a distinct shift in the PDF curve during the pre and post 1980 periods. Moreover, we have analysed the relationship of various climate indices (Niño3.4, Modoki, DMI, PDO, AMO, and AZM) with the mean and extreme rainfalls in addition to the spatial trends. Most of the indices show a significant inverse relationship with mean and extreme rainfalls. The areas of significant correlation are less in the case of extreme rainfall while comparing with that of the mean rainfall.

Ramification of Global and Local Climatic Variability on Resurgent Cases of Dengue in Delhi, India

It has become evident that the global climate is changing rapidly over the past few decades. The variation and change in the global climatic factors have a notable impact on the local climate of a region. The changing climate is widely regarded as one of the most serious global health threats of the 21st century. Among various kinds of diseases, the most vulnerable to these changes are vector-borne diseases. In the Indian context, particularly Delhi city is the most vulnerable to dengue, a kind of vector-borne disease having its highest impact. We sought to identify and explore the correlation and influence of the global climatic phenomena and local climatic factors with the reported number of dengue cases in Delhi. The temporal expansions of reported dengue cases in Delhi have a variation from its first major outbreak in the city during the year 1996 to 2015. A statistical tool like Pearson Product Moment Correlation (PPMC) is used in this study to establish the interrelationship and the level of impact and local climatic variation on dengue. An exceptional negative correlation value of r = -0.82 between the monsoon index and the dengue incidences was reported during the positive years and also maintains a very high positive correlation with other global climatic indices. The study here finds that there is a strong correlation of climatic variation which further influences the epidemiology of dengue in Delhi.

A workflow to address pitfalls and challenges in applying machine learning models to hydrology

Data-driven modeling with machine learning (ML) algorithms in hydrologic modeling and forecasting suffers a number of pitfalls and challenges, including variable selection bias, resubstitution validation, inconsistent validation processes for different algorithms and model selection using the test set. They lead to incorrect model development and biased, overly optimistic performance estimates – and thus, unreliable models. This study presents a novel model building and testing workflow that addresses common machine learning (ML) challenges and pitfalls. The presented workflow incorporates optional variable transformation and preprocessing techniques, and applies to various ML model types, variable selection algorithms and resampling techniques. We demonstrate its performance through streamflow forecasting for the Bow River Basin, Alberta, Canada, using four conventional ML algorithms (ANN, SVM, ELM and RBF networks) driven by local hydrometeorological conditions and large-scale climate indices. Using the cross-validation average out-of-sample results and a separate test set, the prediction accuracy estimate bias (the relative difference between the model performance estimated using the validation sets and a separate test set) was empirically estimated to be 5.6%, 4.4%, 2.5% and 3.0% for the Seasonal, May, June and July models, respectively. In addition, the streamflow forecasting models had an average coefficient of determination of 0.85. Preprocessing and dimensionality reduction through principal component analysis (PCA) was detrimental to prediction accuracy. Snow water equivalent from individual snow courses proved the most important predictor for Bow River streamflow, while global climate indices, including the PDO, AMO and PNA, increased the Nash–Sutcliffe efficiency by 6% to 50%. Finally, although forecasting skill decreased with increasing forecast lead time, satisfactory forecasts (NSE>0.5) could be obtained two months in advance of the spring melt, at the end of February. Extensions of this study should address the tendency of different variable selection algorithms to pick irrelevant or redundant predictors after changes in training data.

The Cyclonic Mode of Arctic Ocean Circulation

AbstractArctic Ocean surface circulation change should not be viewed as the strength of the anticyclonic Beaufort Gyre. While the Beaufort Gyre is a dominant feature of average Arctic Ocean surface circulation, empirical orthogonal function analysis of dynamic height (1950–89) and satellite altimetry–derived dynamic ocean topography (2004–19) show the primary pattern of variability in its cyclonic mode is dominated by a depression of the sea surface and cyclonic surface circulation on the Russian side of the Arctic Ocean. Changes in surface circulation after Arctic Oscillation (AO) maxima in 1989 and 2007–08 and after an AO minimum in 2010 indicate the cyclonic mode is forced by the AO with a lag of about 1 year. Associated with a one standard deviation increase in the average AO starting in the early 1990s, Arctic Ocean surface circulation underwent a cyclonic shift evidenced by increased spatial-average vorticity. Under increased AO, the cyclonic mode complex also includes increased export of sea ice and near-surface freshwater, a changed path of Eurasian runoff, a freshened Beaufort Sea, and weakened cold halocline layer that insulates sea ice from Atlantic water heat, an impact compounded by increased Atlantic Water inflow and cyclonic circulation at depth. The cyclonic mode’s connection with the AO is important because the AO is a major global scale climate index predicted to increase with global warming. Given the present bias in concentration of in situ measurements in the Beaufort Gyre and Transpolar Drift, a coordinated effort should be made to better observe the cyclonic mode.

Examination and comparison of binary metaheuristic wrapper-based input variable selection for local and global climate information-driven one-step monthly streamflow forecasting

The use of data-driven models to forecast streamflow has received substantial attention from scholars in recent years. However, systematic studies have not been performed to examine binary metaheuristic wrapper-based input variable selection (BMWIVS) in real-world streamflow forecasting. In this study, we explored binary metaheuristic-based shallow machine learning wrappers for one-step monthly streamflow forecasting using local weather information and global climate indices from three catchments with different hydroclimatic conditions. First, the maximal information coefficient (MIC) was employed to investigate the correlations among the forecasting target, streamflow and candidate input variables, which included both local and global climate information. Then, the BMWIVS models obtained by combining eight binary metaheuristic algorithms, five commonly used shallow machine learning algorithms, two combined filter-based input variable selection (FIVS) methods, and two forecasting methods were examined. Finally, the performance of each model was compared with the performance of typical benchmark models, including the univariate seasonal autoregressive integrated moving average model, five machine learning algorithms with no input variable selection, and five machine learning algorithms that use five different FIVS methods. The experimental results emphasized three significant findings. First, an appropriate input variable selection method should be selected in practice because several examined wrappers were inferior to the benchmark models. Second, the BMWIVS model that combined the regularized extreme learning machine method, binary gray wolf optimizer, FIVS results-based initialization method, and forecasted values averaged over multiple runs yielded the best performance in the three cases studied. Third, the correlations in terms of the MIC between the global climate indices and streamflow were lower than those between local weather information and streamflow, and the best wrapper and FIVS would select more local weather information variables than global climate index variables, which suggests that global climate information can be complementary to local weather information for one-step monthly streamflow forecasting. These findings have remarkable practical applications for forecasting monthly streamflow.

Perceiving the Trend of Terrestrial Climate Change during the Past 40 year (1978-2018)

In past few decades, climate has manifested numerous shifts in its trend. Various natural and anthropogenic factors have influenced the dynamics and the trends of climate change at longer time scale. To understand the long term climate fluctuations, we have analyzed forty years (1978 - 2018) data of ten climatic parameters that are responsible to influence the climate dynamics. The parameters involved in the present study are total solar irradiance (TSI), ultra violet (UV) index, cloud cover, carbon dioxide (CO2) abundances, multivariate (ENSO) index, volcanic explosivity index (VEI), global surface temperature (GST) anomaly, global sea ice extent, global mean sea level and global precipitation anomaly. Using the above mentioned climate entities; we have constructed a proxy index to study the quantitative measure of the climate change. In this process these indicators were aggregated to a single proxy index as global climate index (GCI) that has measured the strength of present climate change in semblance with the past natural variability. To construct GCI, the principal component analysis (PCA) has been used on yearly based data for the period 1978 - 2018. Actually PCA is a statistical tool with which we can reduce the dimensionality of the data and it retains most of the variation in the new data set. Further, we have confined our study to natural climate drivers and anthropogenic climate drivers. Our result has indicated that the strongest climate change has been occurred globally by the end of the year 2018 in comparison to late 1970’s natural variability.

Spatio-temporal characterization of landscape fire in relation to anthropogenic activity and climatic variability over the Western Himalaya, India

ABSTRACT The frequent occurrence of a forest fire can be detrimental to the functioning of ecologically fragile regions such as the Western Himalaya. This study assesses the spatio-temporal variability and trends of fire occurrence in forests, shrubland, and grassland in relation to anthropogenic activities and climate oscillations over Uttarakhand and Himachal Pradesh; the most fire-affected states in the Western Himalaya. The Pre-monsoon (March to June) temporal distribution of fire occurrence is bimodal with the first and second fire peaks occurring in the last week of April and late May/early June, respectively. The length of fire season was found to have reduced by ~ 10 days in 2010s compared to 2000s with the increasing burned area concentration over a shorter period (~110 days). The Mann-Kendall Tau (τ) and Sen’s-slope suggested an increasing trend (τ = 0.3, Sen’s slope = 46.78 km2 year−1, p = 0.02) of burn area over Uttarakhand where most fire activity occurs. The Getis-Ords (Gi*) statistic was derived by using Fire Radiative Power (FRP) data to infer the spatial distribution of hot and cold clusters which were found to be greatest (Gi* = 1–10) in evergreen needleleaf forest which also accounted for a marginally higher mean per-pixel FRP than other land cover types. Analyses of the role of climatic variables on fire activity revealed moderate positive and negative correspondence between the burned area and average maximum-temperature (R = 0.57, p = 0.01) and total precipitation (R = – 0.53, p = 0.02), respectively. Assessment of the variation of fire activity with global climatic oscillation indices found the Niño-3.4 index to have the strongest, albeit modest, relationship (R = 0.51) with burned area anomalies. This study highlights the increase in fire intensity in the fragile forests of the Western Himalayan region and the requirement for further research into the role of climate and anthropogenic activities on fire occurrence in order to best preserve regional biodiversity and develop an effective forest management strategy.

Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting

Input variable selection plays a key role in data-driven streamflow forecasting models. In this study, we propose a two-stage wrapper model to drive one-month-ahead streamflow forecasting in the context of high-dimensional candidate input variables. Initially, the Boruta algorithm, a feature selection method, was applied to select all the relevant input variables for the streamflow series. Then, a novel binary grey wolf optimizer (BGWO)-regularized extreme learning machine (RELM) wrapper was derived. We carried out experiments on two US catchments with 132 candidate input variables, including local meteorological information, global climatic indices, and lags of the streamflow series. Furthermore, the sensitivities of the proposed model in terms of the optimal objective function were compared. The results indicate two important findings. First, the proposed model outperformed commonly used models in terms of four error evaluation criteria. Second, for the proposed model, the root mean square error is a more suitable criterion than the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for the optimal objective function. These findings are of great reference value for developing ELM models for streamflow forecasting.

Intense east coast lows and associated rainfall in eastern Australia

East coast lows (ECLs) are low pressure systems that occur near the east coast of Australia. But not all lows cause the same level of impact, and a small proportion of ECLs are responsible for more than half of all days with widespread rainfall above 50 mm in this region. In this study, we combine analyses of cyclones at both the surface and 500 hPa levels to assess the locations of cyclones responsible for widespread heavy rainfall on the east coast. We found that the majority of days with widespread totals above 100 mm on the east coast occur when a low at 500 hPa over inland southeast Australia coincides with a surface low located more directly over the east coast. Such events occur on about 15 days per year but are responsible for more than 50% of days with widespread heavy rainfall on the eastern seaboard of Australia. We also found that extreme rainfall was most likely when both the surface and upper cyclones were very strong, when measured using the maximum Laplacian of pressure/height. The seasonal frequency of cyclones at the surface and 500 hPa were found to be only weakly correlated with each other and often had opposing relationships (albeit weak in magnitude) with both global climate drivers and indices of local circulation variability. Trends in cyclone frequency were weak over the period 1979–2019, but there was a small decline in the frequency of deep cyclone days, which was statistically significant in some parts of the southeast. Understanding which ECLs are associated with heavy rainfall will help us to better identify how future climate change will influence ECL impacts.

USING GLOBAL CLIMATE INDICES TO PREDICT RAINFALL AND SUGARCANE PRODUCTIVITY IN DRYLANDS OF BANYUWANGI, EAST JAVA, INDONESIA

<p>In Indonesia, sugarcane (<em>Saccharum officinarum </em>L.) is mostly cultivated in drylands, thus depending on rainfall for crop growth and development. Rainfall is an essential factor affecting sugarcane productivity. The global climate indices can be used to investigate potential of rainfall within a given area and its relationship with crop productivity. This reserach aimed to analyze the relationship between the global climate index, rainfall, and sugarcane productivity in drylands near Glenmore sugar mill, i.e., Benculuk and Jolondoro, Banyuwangi, East Java, Indonesia. The global climate index data used were the Southern Oscillation Index (SOI) and Sea Surface Temperature (SST) between 1995 and 2014. Results of this research showed that SOI and SST can be used to predict the rainfall in both Benculuk and Jolondoro. Rainfall (y) can be predicted with SST data (x) using the equation of y = -352.49x + 7724.1 in Benculuk and y = -107.32 + 3443.4 in Jolondoro, as well as with SOI data (x) using the equation of y = 38.664x + 1555.1 in Benculuk and y = 10.541x + 1567.8 in Jolondoro. Sugarcane productivity (y) in Jolondoro can be predicted using data of total rainfall (x) between October and March with the following equation: y = -0.1672x + 1157.3. This equation can be used by sugar mills, sugarcane growers, and other sugarcane-relevant stakeholders for determining the appropriate growing season.</p>

Medium-Term Rainfall Forecasts Using Artificial Neural Networks with Monte-Carlo Cross-Validation and Aggregation for the Han River Basin, Korea

In this study, artificial neural network (ANN) models were constructed to predict the rainfall during May and June for the Han River basin, South Korea. This was achieved using the lagged global climate indices and historical rainfall data. Monte-Carlo cross-validation and aggregation (MCCVA) was applied to create an ensemble of forecasts. The input-output patterns were randomly divided into training, validation, and test datasets. This was done 100 times to achieve diverse data splitting. In each data splitting, ANN training was repeated 100 times using randomly assigned initial weight vectors of the network to construct 10,000 prediction ensembles and estimate their prediction uncertainty interval. The optimal ANN model that was used to forecast the monthly rainfall in May had 11 input variables of the lagged climate indices such as the Arctic Oscillation (AO), East Atlantic/Western Russia Pattern (EAWR), Polar/Eurasia Pattern (POL), Quasi-Biennial Oscillation (QBO), Sahel Precipitation Index (SPI), and Western Pacific Index (WP). The ensemble of the rainfall forecasts exhibited the values of the averaged root mean squared error (RMSE) of 27.4, 33.6, and 39.5 mm, and the averaged correlation coefficient (CC) of 0.809, 0.725, and 0.641 for the training, validation, and test sets, respectively. The estimated uncertainty band has covered 58.5% of observed rainfall data with an average band width of 50.0 mm, exhibiting acceptable results. The ANN forecasting model for June has 9 input variables, which differed from May, of the Atlantic Meridional Mode (AMM), East Pacific/North Pacific Oscillation (EPNP), North Atlantic Oscillation (NAO), Scandinavia Pattern (SCAND), Equatorial Eastern Pacific SLP (SLP_EEP), and POL. The averaged RMSE values are 39.5, 46.1, and 62.1 mm, and the averaged CC values are 0.853, 0.771, and 0.683 for the training, validation, and test sets, respectively. The estimated uncertainty band for June rainfall forecasts generally has a coverage of 67.9% with an average band width of 83.0 mm. It can be concluded that the neural network with MCCVA enables us to provide acceptable medium-term rainfall forecasts and define the prediction uncertainty interval.

Monthly Precipitation Forecasting in the Han River Basin, South Korea, Using Large-Scale Teleconnections and Multiple Regression Models

In this study, long-term precipitation forecasting models capable of reflecting constantly changing climate characteristics and providing forecasts for up to 12 months in advance were developed using lagged correlations with global and local climate indices. These models were applied to predict monthly precipitation in the Han River basin, South Korea. Based on the lead month of forecast, 10 climate indices with high correlations were selected and combined to construct four-variable multiple regression models for monthly precipitation forecasting. The forecast results for the analytical period (2010–2019) showed that predictability was low for some summer seasons but satisfactory for other seasons and long periods. In the goodness-of-fit test results, the Nash–Sutcliffe efficiency (0.48–0.57) and the ratio of the root mean square error to the standard deviation of the observation (0.66–0.72) were evaluated to be satisfactory while the percent bias (9.4–15.5%) was evaluated to be between very good and good. Due to the nature of the statistical models, however, the predictability is highly likely to be reduced if climate phenomena that are different from the statistical characteristics of the past appear in the forecast targets or predictors. The forecast results were also presented as tercile probability information (below normal, normal, above normal) through a comparison with the observation data of the past 30 years. The results are expected to be utilized as useful forecast information in practice if the predictability for some periods is improved.

Relationships among Global Climate Indices and Rainfall Pattern to Detect Impact of Climate Change in Yogyakarta Special Region, Indonesia

Indications of climate change can be known from the changes in rainfall distribution pattern and its volume. Yogyakarta Special Region lately often experiences drought, and has an impact on decreasing agriculture productivity and crop failure in several districts. Some of the research that has been done has not reviewed specifically in the Special Region of Yogyakarta with several Global Climate Indices and has not been spatially displayed. The aims of this research is to determine the relationship between the global climate indices with rainfall patterns in the Special Region of Yogyakarta. Global Climate Indices data used are: Southern Oscillation Index (SOI), Sea Surface Temperature Nino 3.4, Sea Surface Temperature Nino West, and Indian Ocean Basin-wide Warming (IOBW), and rainfall data for 2009-2013. The analysis process uses Principal Component Analysis (PCA) method to determine the relationship between the Global Climate Index and rainfall patterns and then change the data to be spatial using GIS. Based on the results, overall rainfall is negatively correlated with SOI, the area with a quite strong negative correlation (> -0.462) in the northernmost areas namely Cangkringan, Pakem and Ngemplak Districts, and the correlation value continues to decline to the southern regions. The correlation of rainfall with SST Nino 3.4 is dominantly negatively correlated and the area with strong enough negative correlation (> -0.492) in the northern and southernmost regions, namely Pakem, Cangkringan, Ngemplak, Saptosari, Tanjungsari, and Girisubo Districts, and the correlation value continues to decrease to the middle area. Then the correlation of rainfall with Nino West overall has a positive value, the northern region has a strong correlation (> -0.455) in the Districts of Pakem, Cangkringan and Ngemplak, while other regions have a strong enough correlation (> -0.455). The last correlation is with IOBW, it is divided into 2 areas that have positive correlations in the north and south areas and the negative correlation of the middle part is almost entirely, areas with strong positive correlations (> 0.57), namely in Tanjungsari and Girisubo Districts and areas with strong negative correlations (-0,556) in Pundong District. Thus it can be concluded that the Global Climate Indices (SOI, SST Nino 3.4, Nino West, and IOBW) influences the pattern of rainfall distribution in the Special Region of Yogyakarta.

Spatial variation in long‐lead predictability of summer monsoon rainfall using a time‐varying model and global climatic indices

AbstractLong‐lead prediction of summer monsoon rainfall in India is a challenging task, especially at finer spatial scale. The spatial variability in the long‐lead (one or two season in advance) prediction plays a vital role in planning of hydrological and agricultural aspects of the society. One of the major issues in this field is the climate‐induced time‐varying characteristics (non‐stationarity) that lead to a deteriorating model performance as the time passes by after the model calibration. This study proposes the use of time‐varying approaches in order to check such deteriorating performance over time. Considering the time‐varying association between Indian summer monsoon rainfall and large‐scale climatic indices (e.g., El Niño‐Southern Oscillation, Equatorial Indian Ocean Oscillation, North Atlantic Oscillation, Pacific Decadal Oscillation, and El Niño Modoki Index), a time‐varying approach based on hybrid graphical modelling (GM) and vine copula (GM‐Copula) is demonstrated for rainfall prediction over five homogeneous monsoon regions (HMRs) in India. The time‐varying characteristic is imparted in the GM‐Copula approach by recursively updating the model inputs and the corresponding model parameters at a regular time interval (τ) through recalibration. In the time‐varying framework, the parameter τ is referred to as optimum recurrence interval of model recalibration and it is identified as 5 years for the regions with moderate rainfall and 3 years for regions with above and below moderate rainfall. The developed time‐varying approach is able to yield reasonably good prediction performance (mean absolute percentage error being within 4–10% across HMRs) with a prediction lead time of 5 months. HMR‐wise seasonal rainfall predictions with such quality and lead time are expected to be highly useful.

Tactical departures and strategic arrivals: Divergent effects of climate and weather on caribou spring migrations

AbstractThe Arctic has been warming rapidly, affecting ecological processes across the region. Caribou and reindeer (Rangifer tarandus) is a keystone Arctic species undergoing declines in many parts of its range, but definitive links between climate and populations remain elusive. The conspicuous and dramatic mass migration of many caribou populations, during which nearly all pregnant females move from wintering ranges to calving grounds shortly before giving birth, may be an important link between climate and caribou populations. The drivers of migration, however, are similarly mysterious. It is unknown, for example, whether caribou respond to immediate phenological cues, anticipate conditions on calving grounds, or are driven by lagged effects related to physical condition. To investigate the drivers of migration, we analyzed movement data from over 1000 individual caribou from seven major herds, spanning 3000 km across Alaska, Yukon, Northwest Territories (NWT), and Nunavut in Canada, from 1995 to 2017. We developed a hierarchical model to estimate migration departure and arrival times, and analyzed these variables against global climate indices and local weather conditions, exploring immediate and lagged effects, as well as snowmelt timing and vegetation indices. We discovered a continent‐wide synchrony in spring migration departure times, driven mainly by large‐scale, ocean‐driven climate indices (Pacific Decadal Oscillation, Arctic Oscillation, and North Atlantic Oscillation). However, we also found that the speed of migration was highly plastic with later migration departure times followed by shorter migration durations. This plasticity made arrival timing independent of departure timing and its respective drivers. Rather, arrival timing depended strongly on weather conditions from the previous summer: cooler and windier summers generally led to earlier arrival at calving grounds the following year. We suggest that maternal body condition, mainly influenced by conditions that limit insect harassment, is a major factor for earlier spring migration arrival timing, and therefore earlier calving and higher survival rates. We place these results in the context of mechanistic links between climate and caribou population dynamics. Long‐term and large‐scale observations of migratory animals can provide insights into the mechanisms by which long‐distance, collective migrants may adapt to dynamic and unpredictable environments.

On the use of indices to study extreme precipitation on sub-daily and daily timescales

While there are obstacles to the exchange of long-term high temporal resolution precipitation data, there have been fewer barriers to the exchange of so-called ‘indices’. These are derived from daily and sub-daily data and measure aspects of precipitation frequency, duration and intensity that could be used for the study of extremes. This paper outlines the history of the rationale and use of these indices, the types of indices that are frequently used and the advantages and pitfalls in analysing them. Moving forward, satellite precipitation products are now showing the potential to provide global climate indices to supplement existing products using longer-term in situ gauge records but we suggest that to advance this area differences between data products, limitations in satellite-based estimation processes, and the inherent challenges of scale need to be better understood.

Study of rainfall variabilities in Southeast Asia using long-term gridded rainfall and its substantiation through global climate indices

This study utilized a long-term (1951–2014) moderate-resolution observed gridded (0.5°×0.5°) rainfall dataset to analyze long-term and short-term rainfall variability and the seasonality in Southeast Asia (SEA). The previous studies for this region revealed significant enhancement in extreme events and seasonal variabilities in rainfall. In this study, the frequency and intensity-based rainfall extremity analysis was carried out by utilizing a number of rainfall extreme indices (e.g. Dry Days, Wet Days, 90P, 95P, and 99P). A seasonal non-parametric Mann-Kendall test was applied to detect significant increase and decrease of rainfall. The possible causes for the acceleration and variabilities of rainfall were investigated by analyzing the impact of large-scale ocean-atmospheric climate interactions through global climate indices such as El Nino (3.4 and 4.0), Southern Oscillation Index (SOI), Madden-Julian Oscillation (MJO), global mean land and ocean temperature index, QBO and NOI. Our results displayed a significant increase of rainfall amount in most of SEA regions, while a minimal decrease was also found over some regions. The rainfall indices displayed substantial intra-decadal variabilities over the SEA region. The intra-decadal percent of change analysis based on rainfall extreme indices revealed significant changes in rainfall extremes over SEA, which also evidenced the computed acceleration in rainfall over the region. The correlation analysis based findings exhibited that a strong correlation existed between the SEA rainfall and the large-scale global climate indices generated based on the ocean-atmosphere climate interactions. After regionalization of the SEA, the global climate indices based results described the spatiotemporal variabilities in rainfall, which were computed over SEA regions. The global climate indices such as El Nino (3.4 & 4.0), SOI, NOI and MJO suggested significant correlations with the seasonal rainfall and the rainfall extreme indices and highlighted that their collinearities vary within regions as per variations in the monsoon.

Application of a neural network model to forecasting of El Niño and La Niña

In this paper, a possibility to forecast El Niño and La Niña by using an artificial intelligence model based on neural networks is studied. The quality of such a long-term climate forecast is assessed too. A set of global climatic indices of atmosphere-ocean system oscillations in 1950-2019 is used as input parameters of the model. The Nino3.4 index is calculated by using monthly average 500mb geopotential height and sea surface temperature fields from NCEP/NCAR reanalysis data sets. A verification of the model is carried out by using a control sample of 1950–1957. Additionally, the same indices of 1872-1947 are calculated by using 20th Century Reanalysis (20CR) data sets to test the model. A possibility to predict the Nino3.4 index for 2 to 7 months is shown. However, in spite of a high-level reconstruction of the index dynamics, increasing the time of forecasting is accompanied by decreasing its quality. With 20CR data it is shown that the model is able to successfully predict the beginning of 75% of El Niño and La Niña for 3 months, 66% of the events for 5 months, and only 52% for 7 months in advance.