Large-scale Climate Modes Research Articles

Rapid decline in extratropical Andean snow cover driven by the poleward migration of the Southern Hemisphere westerlies

Seasonal snow in the extratropical Andes is a primary water source for major rivers supplying water for drinking, agriculture, and hydroelectric power in Central Chile. Here, we used estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) to analyze changes in snow cover extent over the period 2001–2022 in a total of 18 watersheds spanning approximately 1,100 km across the Chilean Andes (27–36°S). We found that the annual snow cover extent is receding in the watersheds analyzed at an average pace of approximately 19% per decade. These alarming trends have impacted meltwater runoff, resulting in historically low river streamflows during the dry season. We examined streamflow records dating back to the early 1980s for 10 major rivers within our study area. Further comparisons with large-scale climate modes suggest that the detected decreasing trends in snow cover extent are likely driven by the poleward migration of the westerly winds associated with a positive trend in the Southern Annular Mode (SAM).

Improvement of decadal predictions of monthly extreme Mei-yu rainfall via a causality guided approach

Abstract While the improved performance of climate prediction systems has allowed better predictions of the East Asian Summer Monsoon rainfall to be made, the ability to predict extreme Mei-yu rainfall (MYR) remains a challenge. Given that large scale climate modes (LSCMs) tend to be better predicted by climate prediction systems than local extremes, one useful approach is to employ causality-guided statistical models (CGSMs), which link known LSCMs to improve MYR prediction. However, previous work suggests that CGSMs trained with data from 1979–2018 might struggle to model MYR in the pre-1978 period. One hypothesis is that this is due to potential changes in causal processes, which modulate MYR in different phases of the multidecadal variability, such as the Pacific decadal oscillation (PDO). In this study, we explore this hypothesis by constructing CGSMs for different PDO phases, which reflect the different phases of specific causal process, and examine the difference in quality as well as with respect to difference drivers and thus causal links between CGSMs of different PDO phases as well as the non-PDO phase specific CGSMs. Our results show that the set of predictors of CGSMs is PDO phase specific. Furthermore, the performance of PDO phase specific CGSMs are better than the non-PDO phase specific CGSMs. To demonstrate the added value of CGSMs, the PDO phase specific versions are applied to the latest UK Met Office decadal prediction system, DePreSys4, and it is shown that the root-mean squared errors of MYR prediction based on PDO phase specific CGSMs is consistently smaller than the MYR predicted based on the direct DePreSys4 extreme rainfall simulations. We conclude that the use of a causality approach improves the prediction of extreme precipitation based solely on known LSCMs because of the change in the main drivers of extreme rainfall during different PDO-phases.

Empirical orthogonal function analysis of lightning flashes over India

Lightning studies are highly focused on spatial and temporal variability in various scales but very limited studies are focused on dominant spatial modes of variability. This study intends to identify the possible spatial modes of climate variability of lightning over India during different seasons and relate them to regional and large-scale climate modes. Empirical orthogonal function analysis of lightning has been carried out and the first three orthogonally independent modes are considered in order to retrieve the maximum variance explained by each mode. To understand the role of remote and local teleconnections on the lightning flash rate (LFR) variability, we have analyzed two Pacific Ocean modes (El Niño Southern Oscillation; ENSO, Pacific Decadal Oscillation; PDO) and two Indian Ocean modes (Indian Ocean Dipole; IOD and Bay of Bengal (BOB) meridional Sea Surface Temperature (SST) gradient). First mode is positively correlated with the warm phase of ENSO and PDO whereas second and third modes are negatively correlated with the warm phase of ENSO and PDO during pre-monsoon, post-monsoon and winter. Reverse is true for the monsoon season due to the shift in walker cell caused by the changes in the location of the heat sources and sinks. A strong positive correlation of IOD and BOB meridional SST gradient with first mode, suggests the vital role of nearby Indian Ocean in explaining the typical lightning flashes over India due to the enhanced zonal and meridional circulation, thereby moisture supply to the Indian subcontinent. The impact of Nino-3.4, IOD and BOB meridional SST gradient on lightning over India further suggest the role of SST in local and remote influence on lightning variability through the distribution and transport of heat and moisture.

Variability in flood frequency in sub-Saharan Africa: The role of large-scale climate modes of variability and their future impacts

Sub-Saharan Africa (SSA) is strongly affected by flood hazards, endangering human lives and economic stability. However, the role of internal climate modes of variability in driving fluctuations in SSA flood occurrence remains poorly documented and understood. To address this gap, we quantify the relative and combined contribution of large-scale climate drivers to seasonal and regional flood occurrence using a new 65-year daily streamflow dataset, sea-surface temperatures derived from observations, and 12 Single Model Initial-condition Large Ensembles (SMILEs) from the Coupled Model Intercomparison Project Phases 5 and 6. We find significant relationships between floods and large-scale climate variability across SSA, with climatic drivers accounting for 30–90 % of the variability in floods. Notably, western, central, and the summer-rain region of southern Africa display stronger teleconnections to large-scale climate variability in comparison to East Africa and the winter-rain region of South Africa, where regional circulation patterns and human activities may play a more important role. In southern and eastern Africa, floods are mainly influenced by teleconnections with the Pacific and Indian Oceans, while in western and central Africa, teleconnections with the Atlantic Ocean and Mediterranean Sea play a larger role. We also find that the number of floods is projected to fluctuate by ± 10–50 % during the 21st century in response to different sequences of key modes of climate variability. We also note that the relative contributions of large-scale climate variability to future flood risks are generally consistent across all SMILEs. Our findings thus provide valuable information for long-term disaster risk reduction and management.

Spatially varying impacts of pacific and southern ocean climate modes on tidal residuals in New South Wales, Australia

Still water levels along the New South Wales (NSW) coast often vary from those predicted by harmonic analysis based purely on astronomical forcing. This variation can lead to unexpected inundation episodes along the coast and in estuaries. This study investigates the large-scale ocean-atmosphere drivers that can contribute to these unexpected water levels. Time series regressions and cross- and coherence-wavelet transforms were used to investigate relationships between six tidal residual datasets from NSW ocean gauges and three indices of large-scale climate modes: canonical El Niño Southern Oscillation (ENSO), ENSO Modoki, and Southern Annular Mode (SAM). Significant relationships were identified between canonical ENSO and SAM and the tidal residual datasets; however, ENSO Modoki did not have a significant impact. Collectively, SAM and canonical ENSO account for up to 45% of the variance in tidal residuals along the NSW coast, with impacts increasing southwards. Wavelet coherences showed that tidal residuals covary significantly with SAM and ENSO, with the largest peaks in the two- and eight-year frequency bands. These results demonstrate a strong dependence of tidal residuals on the phasing of SAM and canonical ENSO. With continued improvements in seasonal forecasts of climate modes, including canonical ENSO and SAM, this research presents important new insights that may contribute to improved management of inundation along the NSW coast. The research also provides a baseline of natural variability on which climate change projections can be considered, given that inundation events are projected to become more frequent.

Impacts of large scale climate modes on the current and future bimodal wave climate of a semi-protected shallow gulf

Impacts of large scale climate modes on the current and future bimodal wave climate of a semi-protected shallow gulf

Characterising continental shelf waves and their drivers for the southeast coast of Australia

Low-lying, developed areas in New South Wales (NSW) are susceptible to extended periods of inundation, which cannot be attributed to tides or local synoptic conditions. This inundation is often associated with relatively small water level anomalies but is persistent enough to damage the built environment. Due to their long duration, continental shelf waves (CSWs) are one of the mechanisms of concern associated with extended coastal inundation. These waves are caused by a range of synoptic disturbances occurring along the mid-latitudes and they travel anticlockwise along the coast (in the southern hemisphere), reaching low latitudes of NSW. This study investigates the characteristics of CSWs that travel along the NSW coast, their generation and propagation mechanisms in relation to local synoptic and oceanic conditions, and their potential modulation by two major large-scale climate modes: the El Niño/Southern Oscillation and the Southern Annular Mode (SAM). We used time series of daily tidal residuals from 1994 to 2015 from nine locations along the NSW coast and used a peak over threshold approach to identify 29 waves which had an average amplitude of 0.3 m, mean period of 15 days (ranging from 8 to 20 days), and a highest occurrence in autumn and winter. The generation of CSWs is tightly coupled to the subpolar jet, the belt of westerlies, and the subtropical ridge, confirming that large-scale atmospheric circulation is a key element in the generation and propagation of CSWs. Through analyses of the inverse barometer, we found that CSWs propagate freely up to the north coast of NSW, independent of local weather conditions. We also found that CSWs are more frequent during the positive phase of SAM combined with La Niña and during neutral SAM combined with El Niño events. This study provides the longest database to date of CSWs for NSW, alongside new insights into their characteristics and timing and frequency of occurrence. Importantly, this information can be used to monitor their occurrence and forecast their impact, including the number of days taken for a wave to reach different locations along the NSW coast.

Ability of a dynamical climate sensitive disease model to reproduce historical Rift Valley Fever outbreaks over Africa

Rift Valley Fever (RVF) is a zoonosis transmitted by Aedes and Culex mosquitoes, and is considered a priority pathogen by the WHO. RVF epidemics mostly occur in Africa and can decimate livestock herds, causing significant economic losses and posing health risks for humans. RVF transmission is associated with the occurrence of El Niño events that cause floods in eastern Africa and favour the emergence of mosquitoes in wetlands. Different risk models have been developed to forecast RVF transmission risk but very few studies have validated models at pan-African scale. This study aims to validate the skill of the Liverpool Rift Valley Fever model (LRVF) in reproducing RVF epidemics over Africa and to explore the relationship between simulated climatic suitability for RVF transmission and large-scale climate modes of variability such as the El Niño Southern Oscillation (ENSO) and the Dipole Mode Index (DMI). Our results show that the LRVF model correctly simulates RVF transmission hotspots and reproduces large epidemics that affected African countries. LRVF was able to correctly reproduce major RVF epidemics in Somalia, Kenya, Zambia and to a lesser extent for Mauritania and Senegal. The positive phases of ENSO and DMI are associated with an increased risk of RVF over the Horn of Africa, with important time lags. Following research activities should focus on the development of predictive modelling systems at different time scales.

Understanding the compound marine heatwave and low-chlorophyll extremes in the western Pacific Ocean

The western Pacific Ocean is the global center for marine biodiversity, with high vulnerability to climate change. A better understanding of the spatiotemporal characteristics and potential drivers of compound marine heatwaves (MHWs) and low-chlorophyll (LChl) extreme events is essential for the conservation and management of local marine organisms and ecosystems. Here, using daily satellite sea surface temperature and model-based chlorophyll concentration, we find that the climatological spatial distribution of MHW-LChl events in total days, duration, and intensity exhibits heterogeneous distributions. The southwest sections of the South China Sea (WSCS) and Indonesian Seas are the hotspots for compound events, with total MHW-LChl days that are more than 2.5 times higher than in the other sub-regions. Notably, there is a trend toward more frequent (> 4.2 d/decade), stronger (> 0.5), and longer-lasting (> 1.4 d/decade) MHW-LChl occurrences in the WSCS. The occurrence of compound MHW-LChl extremes exhibits remarkable seasonal differences, with the majority of these events transpiring during winter. Moreover, there are generally statistically significant increasing trends in MHW-LChl events for all properties on both seasonal and inter-annual timescales. Furthermore, we reveal that the total days of compound MHW-LChl extremes are strongly modulated by large-scale climate modes such as the El Niño-Southern Oscillation and Dipole Mode Index. Overall, pinpointing MHW-LChl hotspots and understanding their changes and drivers help vulnerable communities in better preparing for heightened and compounded risks to marine organism and ecosystems under climate change.

Climate influence on compound solar and wind droughts in Australia

Solar photovoltaic and wind power are central to Australia’s renewable energy future, implying an energy sector vulnerable to weather and climate variability. Alignment of weather systems and the influence of large-scale climate modes of variability risks widespread reductions in solar and wind resources, and could induce grid-wide impacts. We therefore systematically analyse the relationship between compound solar radiation and wind speed droughts with weather systems and climate modes of variability over multiple time scales. We find that compound solar and wind droughts occur most frequently in winter, affecting at least five significant energy-producing regions simultaneously on 10% of days. The associated weather systems vary by season and by drought type, although widespread cloud cover and anticyclonic circulation patterns are common features. Indices of major climate modes are not strong predictors of grid-wide droughts, and are typically within one standard deviation of the mean during seasons with the most widespread events. However, the spatial imprints of the teleconnections display strong regional variations, with drought frequencies varying by more than ten days per season between positive and negative phases of climate modes in some regions. The spatial variability of these teleconnection patterns suggests that droughts in one region may be offset by increased resource in another. Our work highlights the opportunity for minimising the impact of energy production variability by utilising weather and climate intelligence. Exploiting the spatial variability associated with daily weather systems and the seasonal influence of climate modes could help build a more climate-resilient renewables-dominated energy system.

Analysis of compound floods from storm surge and extreme precipitation in China

To enhance flood protection and risk reduction in China's coastal cities, it is crucial to comprehend the variability and underlying causes of compound floods along the coasts arising from storm surges and intense precipitation. The variability of compound floods from storm surge and extreme precipitation from 1958 to 2014 was analyzed using daily maximum sea level data and daily cumulative precipitation from 16 tide gauges along the coast of China. More than 70 % of the storm surges that caused damage from 1989 to 2014 at 14 out of 16 tide gauges were compound floods. We found a strong and seasonally varying dependence between storm surges and extreme precipitation along the coast of China. There were generally positive trends for the annual number of compound events at tide gauges south of 35°N. Compound flood variability was related to large-scale climate modes. The Arctic Oscillation in March was significantly negatively correlated with the compound events at tide gauges north of 35°N, while the opposite was true at tide gauges south of 35°N. The compound events were significantly correlated with the Pacific Decadal Oscillation (PDO) in June at 10 of the 16 tide gauges, and negatively correlated with the Niño1 + 2 index in February at 7 of the 16 tide gauges. Precipitation extremes determine the changes in the occurrence of compound floods, and by modifying the East Asian summer monsoon and typhoons, the large climate modes are supposed to impact the occurrence of compound floods. Our findings can enhance our understanding of compound floods and provide an important reference for flood risk management, particularly for low-lying coastal cities in China.

Extreme marine heatwaves and cold-spells events in the Southern North Sea: classifications, patterns, and trends

In this study, we examined the long-term spatiotemporal trend of marine heatwaves (MHW) and marine cold spells (MCS) characteristics in the southern North Sea over the last four decades (1982-2021). We then estimated the difference between their annual mean values and the possible relationship with the large-scale climate modes of natural sea surface temperature (SST) and atmospheric variability using satellite SST data. The SST warming rate was 0.33 ± 0.06°C/decade and was associated with an increase in MHW frequency (0.85 ± 0.39 events/decade) and a decrease in MCS frequency (-0.92 ± 0.40 events/decade) over the entire period. We found a distinct difference between the annual mean values of MHW and MCS characteristics, with a rapid increase in total MHW days (14.36 ± 8.16 days/decade), whereas MCS showed an opposite trend (-16.54 ± 9.06 days/decade). The highest MHW frequency was observed in the last two decades, especially in 2014 (8 events), 2020 (5 events), and 2007 (4 events), which were also the warmest years during the study period. Only two years (2010 and 2013) in the last two decades had higher MCS frequency, which was attributed to the strong negative phase of the North Atlantic Oscillation (NAO). Our results also show that on the annual scale, both the East Atlantic Pattern (EAP) and the Atlantic Multidecadal Oscillation (AMO) play a more important role in the formation of the MHW in the southern North Sea than the other teleconnections (e.g., the NAO). However, the NAO made the largest contribution only in the winter. Strong significant (p < 0.05) positive/negative correlations were found between oceanic and atmospheric temperatures and the frequency of MHW/MCS. This suggests that with global warming, we can expect an increase/decrease in MHW/MCS occurrences in the future.

Predictability of Marine Heatwaves off Western Australia Using a Linear Inverse Model

Abstract Marine heatwaves (MHWs) off Western Australia (110°–116°E, 22°–32°S; herein, WA MHWs) can cause devastating ecological impacts, as was evidenced by the 2011 extreme event. Previous studies suggest that La Niña is the major large-scale driver of WA MHWs, while the Indian Ocean dipole (IOD) may also play a role. Here, we investigate historical WA MHWs and their connections to these large-scale climate modes in an ocean model (ACCESS-OM2) simulation driven by a prescribed atmosphere from JRA-55-do over 1959–2018. Rather than analyzing sea surface temperature, the WA MHWs and climate mode indices were characterized and investigated in vertically averaged temperature (VAT) to ∼300-m depth to afford the longer ocean dynamic time scales, including remote oceanic connections. We develop a cyclostationary linear inverse model (CS-LIM; from 35°S to 10°N, across the Indo-Pacific Ocean), to investigate the relative contributions of La Niña VAT and positive IOD VAT to the predictability of WA VAT MHWs. Using a large ensemble of CS-LIM simulations, we found that ∼50% of WA MHWs were preceded about 5 months by La Niña, and 30% of the MHWs by positive IOD about 20 months prior. While precursor La Niña or positive IOD, on their own, were found to correspond with increased WA MHW likelihood in the months following (∼2.7 times or ∼1.5 times more likely than by chance, respectively), in combination these climate mode phases were found to produce the largest enhancement in MHW likelihood (∼3.2 times more likely than by chance). Additionally, we found that stronger and longer La Niña and/or positive IOD tend to lead stronger and longer WA MHWs. Significance Statement This study examines seasonal-to-interannual time-scale predictability of marine heatwaves off Western Australia. We developed and applied a linear inverse model, informed by numerical model results, to generate a large number of 60-yr temperature simulations across the broader Indian–Pacific Ocean region to quantify this marine heatwave predictability. We found that La Niña typically increases the likelihood of marine heatwaves off Western Australia about 5 months (3–7 months) later, while positive Indian Ocean dipole events increase their likelihood about 20 months (18–22 months) later. Marine heatwaves can severely impact local marine ecosystems and the economy. Our findings are expected to be valuable for marine heatwave prediction system development on time scales that can be beneficial to marine ecosystem conservation and fishery management.

The influence of large-scale climate modes on tropical cyclone tracks in the southwest Pacific

Tropical cyclones (TCs) impact the economy, properties, lives and infrastructure of island nations and territories of the southwest Pacific (SWP), accounting for three in four regional disasters each year. To increase the resilience of the SWP to the destructive impacts of TCs, improved TC track forecasts are needed since a high degree of uncertainty exists around the likely path a TC will take in this region post-formation. This requires better comprehension of the factors contributing to TC track variability occurring at different timescales. Therefore, we examine the modulating impact of key Indo-Pacific climate drivers: the El Niño-Southern Oscillation (ENSO), Interdecadal Pacific Oscillation (IPO), Southern Annular Mode (SAM) and the Indian Ocean Dipole (IOD), on SWP TC track variability. We present new insights into the spatial (i.e. prevailing trajectories) and temporal (i.e. track length, average speed and duration) components of TC tracks, being modulated by both individual and combined climate modes. Overall, TC tracks tend to shift northeast during El Niño, IPO positive, IOD east positive and/or SAM negative phases (with a southwest shift observed during the opposite climate phases). Further, we show that when two of these climate modes are in their positive phase (e.g. El Niño with the positive phases of IPO or eastern pole of IOD and SAM), TC track length and average speed are enhanced. However, for cases where either one (e.g. El Niño/negative phase of IPO and IOD east) or two (La Niña/negative phase of IPO, IOD east and SAM) climate modes were in the negative phase, an increase in TC track duration was observed. The findings of this study may be used to improve TC forecasting and better quantify TC-related risks.

Compound Climate Risk: Diagnosing Clustered Regional Flooding at Inter-Annual and Longer Time Scales

The potential for extreme climate events to cluster in space and time has driven increased interest in understanding and predicting compound climate risks. Through a case study on floods in the Ohio River Basin, we demonstrated that low-frequency climate variability could drive spatial and temporal clustering of the risk of regional climate extremes. Long records of annual maximum streamflow from 24 USGS gauges were used to explore the regional spatiotemporal patterns of flooding and their associated large-scale climate modes. We found that the dominant time scales of flood risk in this basin were in the interannual (6–7 years), decadal (11–13 years), and secular bands and that different sub-regions within the Ohio River Basin responded differently to large-scale forcing. We showed that the leading modes of streamflow variability were associated with ENSO and secular trends. The low-frequency climate modes translated into epochs of increased and decreased flood risk with multiple extreme floods or the absence of extreme floods, thus informing the nature of compound climate-induced flood risk. A notable finding is that the secular trend was associated with an east-to-west shift in the flood incidence and the associated storm track. This is consistent with some expectations of climate change projections.

Synchronous climate hazards pose an increasing challenge to global coffee production

Global coffee production is at risk from synchronous crop failures, characterised by widespread concurrent reductions in yield occurring in multiple countries at the same time. For other crops, previous studies have shown that synchronous failures can be forced by spatially compounding climate anomalies, which in turn may be driven by large-scale climate modes such as the El Niño Southern Oscillation (ENSO). We provide a systematic analysis of spatially compounding climate hazards relevant to global coffee production. We identify 12 climate hazards from the literature, and assess the extent to which these hazards occur and co-occur for the top 12 coffee producing regions globally. We find that the number of climate hazards and compound events has increased in every region between 1980 and 2020. Furthermore, a clear climate change signature is evident, as the type of hazard has shifted from overly cool conditions to overly warm. Spatially compounding hazards have become particularly common in the past decade, with only one of the six most hazardous years occurring before 2010. Our results suggest that ENSO is the primary mode in explaining annual compound event variability, both globally and regionally. El Niño-like sea-surface temperatures in the Pacific Ocean are associated with decreased precipitation and increased temperatures in most coffee regions, and with spatially compounding warm and dry events. This relationship is reversed for La Niña-like signatures. The Madden Julian Oscillation also shows a strong association with climate hazards to coffee, with increased activity in the Maritime Continent related to a global increase in the number of cold or wet hazards and a decrease in the number of warm or dry hazards. With climate change projections showing a continued rise in temperatures in the tropics is likely, we suggest that coffee production can expect ongoing systemic shocks in response to spatially compounding climate hazards.

Seasonal and Year-To-Year Variability of Boundary Currents and Eddy Salt Flux along the Eastern and Southern Coasts of Sri Lanka Observed by PIES and Satellite Measurements

Abstract Boundary currents along the Sri Lankan eastern and southern coasts serve as a pathway for salt exchange between the Bay of Bengal and the Arabian Sea basins in the northern Indian Ocean, which are characterized by their contrasting salinities. Measurements from two pairs of pressure-sensing inverted echo sounders (PIES) deployed along the Sri Lankan eastern and southern coasts as well as satellite measurements are used to understand the variability of these boundary currents and the associated salt transport. The volume transport in the surface (0–200-m depth) layer exhibits a seasonal cycle associated with the monsoonal wind reversal and interannual variability associated with the Indian Ocean dipole (IOD). In this layer, the boundary currents transport low-salinity water out of the Bay of Bengal during the northeast monsoon and transport high-salinity water into the Bay of Bengal during the fall monsoon transition of some years (e.g., 2015 and 2018). The Bay of Bengal salt input increases during the 2016 negative IOD as the eastward flow of high-salinity water during the fall monsoon transition intensifies, whereas the effect of the 2015/16 El Niño on the Bay of Bengal salt input is still unclear. The time-mean eddy salt flux over the upper 200 m estimated for the April 2015–March 2019 period along the eastern coast accounts for 9% of the salt budget required to balance an estimated 0.13 Sv (1 Sv ≡ 106 m3 s−1) of annual freshwater input into the Bay of Bengal. The time-mean eddy salt flux over the upper 200 m estimated for the December 2015–November 2019 period along the southern coast accounts for 27% of that same salt budget. Significance Statement In the northern Indian Ocean, the highly saline Arabian Sea undergoes extreme evaporation while the Bay of Bengal (BoB) receives excess freshwater input. The focus of this study is the role of the observed time-variable circulation around Sri Lanka that permits the exchange between these basins to maintain their salinity distributions. The circulation fluctuates seasonally following the monsoon wind reversal and interannually in response to large-scale climate modes. The BoB freshwater export around Sri Lanka occurs during the northeast monsoon, whereas saline water import occurs during the fall monsoon transition of some years. However, rapid changes in both water volume transport and salt exchange can occur. The circulation over 0–200-m depth transports ∼9%–27% of the BoB salt budget.

Impact of historical climate variability on rice production in Mainland Southeast Asia across multiple scales

Climate change poses significant pressure on global food production. Although previous work has explored impacts of climate, management, and genetics on food production, additional research is needed to examine the effects of large-scale climate modes at local and regional scales. This study explores the impact of climate variability on rice yield in Mainland Southeast Asia from 1961-2017 at three different spatial scales: the whole Mainland Southeast Asia region, country-level (Cambodia, Laos, Myanmar, Thailand, and Vietnam), and province-level for Vietnam. Annual rice yields over this period have nearly tripled with Vietnam experiencing the largest increases. Correlations between annual rice yield anomalies at the regional and country levels and climate data reveal clear influences of tropical climate variability associated with the El Niño-Southern Oscillation and the Pacific Meridional Mode. At the provincial level in Vietnam, many provinces show similar correlation patterns for the spring-summer season of rice (e.g., a co-occurring La Niña and positive phase of the Pacific Meridional Mode in the preceding boreal winter and spring are associated with increased yields in spring-summer rice). However, the late summer-fall season rice yield anomalies show much weaker correlations with tropical climate patterns. Variations across provinces were also noted, particularly between the Red River and Mekong River Deltas. The history of this 56-year period, which included the Vietnam-American War and changes in land management policies, makes it challenging to disentangle the effects of climate variability and social factors on rice yields in these areas. However, these results highlight the importance of using a multidisciplinary and multiscale approach to help inform local and regional decision-making. • We examined climate and rice yields in Mainland Southeast Asia at 3 scales. • Correlations revealed clear influences of tropical climate variability. • Provinces in Vietnam showed varying results by rice season and location. • Future climate change may shift planting timing and threaten national exports. • A multidisciplinary and multiscale approach can help disentangle effects on rice.

A Causality-guided Statistical Approach for Modeling Extreme Mei-yu Rainfall Based on Known Large-scale Modes—A Pilot Study

Extreme Mei-yu rainfall (MYR) can cause catastrophic impacts to the economic development and societal welfare in China. While significant improvements have been made in climate models, they often struggle to simulate local-to-regional extreme rainfall (e.g., MYR). Yet, large-scale climate modes (LSCMs) are relatively well represented in climate models. Since there exists a close relationship between MYR and various LSCMs, it might be possible to develop causality-guided statistical models for MYR prediction based on LSCMs. These statistical models could then be applied to climate model simulations to improve the representation of MYR in climate models.In this pilot study, it is demonstrated that skillful causality-guided statistical models for MYR can be constructed based on known LSCMs. The relevancy of the selected predictors for statistical models are found to be consistent with the literature. The importance of temporal resolution in constructing statistical models for MYR is also shown and is in good agreement with the literature. The results demonstrate the reliability of the causality-guided approach in studying complex circulation systems such as the East Asian summer monsoon (EASM). Some limitations and possible improvements of the current approach are discussed. The application of the causality-guided approach opens up a new possibility to uncover the complex interactions in the EASM in future studies.

Global seasonal forecasts of marine heatwaves

Marine heatwaves (MHWs)—periods of exceptionally warm ocean temperature lasting weeks to years—are now widely recognized for their capacity to disrupt marine ecosystems1–3. The substantial ecological and socioeconomic impacts of these extreme events present significant challenges to marine resource managers4–7, who would benefit from forewarning of MHWs to facilitate proactive decision-making8–11. However, despite extensive research into the physical drivers of MHWs11,12, there has been no comprehensive global assessment of our ability to predict these events. Here we use a large multimodel ensemble of global climate forecasts13,14 to develop and assess MHW forecasts that cover the world’s oceans with lead times of up to a year. Using 30 years of retrospective forecasts, we show that the onset, intensity and duration of MHWs are often predictable, with skilful forecasts possible from 1 to 12 months in advance depending on region, season and the state of large-scale climate modes, such as the El Niño/Southern Oscillation. We discuss considerations for setting decision thresholds based on the probability that a MHW will occur, empowering stakeholders to take appropriate actions based on their risk profile. These results highlight the potential for operational MHW forecasts, analogous to forecasts of extreme weather phenomena, to promote climate resilience in global marine ecosystems.