Predictability Of Heat Waves Research Articles

Evaluation of 2010 heatwave prediction skill by SLNE coupled model

Abstract SLNE is the coupled model, that was developed in 2023. SL and NE here are the first two letters from SLAV (Semi-Lagrangian, based on Absolute Vorticity equation) model of the atmosphere and NEMO (Nucleus for European Modelling of the Ocean) ocean model that have been coupled using OASIS3-MCT software. The initial conditions for SLAV and NEMO are specified from an atmospheric and ocean analyses produced in Hydrometcentre of Russia. The 2010–2021 hindcast accuracy study shows, that SLNE has comparable errors to the operational SLAV model on a sub-seasonal time scale. The SLNE model has improved prediction skill of the 2010 heatwave features in comparison to SLAV, that is a motivation for further work to improve the coupled model.

Unveiling teleconnection drivers for heatwave prediction in South Korea using explainable artificial intelligence

Increasing heatwave intensity and mortality demand timely and accurate heatwave prediction. The present study focused on teleconnection, the influence of distant land and ocean variability on local weather events, to drive long-term heatwave predictions. The complexity of teleconnection poses challenges for physical-based prediction models. In this study, we employed a machine learning model and explainable artificial intelligence to identify the teleconnection drivers for heatwaves in South Korea. Drivers were selected based on their statistical significance with annual heatwave frequency ( | R | > 0.3, p < 0.05). Our analysis revealed that two snow depth (SD) variabilities—a decrease in the Gobi Desert and increase in the Tianshan Mountains—are the most important and predictive teleconnection drivers. These drivers exhibit a high correlation with summer climate conditions conducive to heatwaves. Our study lays the groundwork for further research into understanding land–atmosphere interactions over these two SD regions and their significant impact on heatwave patterns in South Korea.

Large-scale Atmospheric Patterns associated with the 2018 Heatwave Prediction in the Korea-Japan Region using GloSea6

Large-scale Atmospheric Patterns associated with the 2018 Heatwave Prediction in the Korea-Japan Region using GloSea6

Probabilistic post-processing of short to mediumrange temperature forecasts: Implications for heatwave prediction in India.

Accurate and reliable air temperature forecasts are necessary for predicting and responding to thermal disasters such as heat strokes. Forecasts from Numerical Weather Prediction(NWP) models contain biases which require post-processing. Studies assessing the skill of probabilistic post-processing techniques (PPTs) on temperature forecasts in India are lacking. This study aims to evaluate probabilistic post-processing approaches such as Nonhomogeneous Gaussian Regression (NGR) and Bayesian Model Averaging (BMA) for improving daily temperature forecasts from two NWP models, namely, the European Centre for Medium Range Weather Forecasts (ECMWF) and the Global Ensemble Forecast System (GEFS), across the Indian subcontinent. Apart from that, the effect of probabilistic PPT on heatwave prediction skills across India is also evaluated. Results show that probabilistic PPT comprehensively outperform traditional approaches in forecasting temperatures across India at all lead times. In the Himalayan regions where the forecast skill of raw forecasts is low, the probabilistic techniques are not able to produce skillful forecasts even though they perform much better than traditional techniques. The NGR method is found to be the best performing PPT across the Indian region. Post-processing Tmax forecasts using the NGR approach was found to considerably improve the heatwave prediction skill across highly heatwave prone regions in India. The outcomes of this study will be helpful in setting up improved heatwave prediction and early warning systems in India.

Statistical downscaling of maximum temperature under CMIP6 global climate models and evaluation of heat wave events using deep learning methods for Indo‐Gangetic Plain

AbstractThe rising global temperature is one of the primary concerns of the world as it impacts the economy, environment and healthcare of any country which are more pronounced a regional level. Assessment of regional impacts of climate change at a local level requires fine resolution of climate data for which a robust and fast downscaling method is needed. In this study, we use three deep learning‐based methods, namely long short‐term memory network (LSTM), deep neural network (DNN) and recurrent neural network (RNN), to downscale CMIP6 13 GCMS models data (1.25° × 1.25° resolution) global climate model (GCM) maximum temperature (Tmax) at a regional scale of 0.5° × 0.5° spatial resolution for the period 1991–2010 over the Indo‐Gangetic Plain (IGP). In addition to the temperature prediction, heat wave events have been also analysed in the study. The study found that LSTM method performs better than DNN and RNN in downscaling of all GCM model datasets when evaluated against observed maximum temperature data from the India Meteorological Department (IMD) in terms of RMSE (0.9–3.5), average of all grid MAE value between (1.2 and 2.68), correlation (0.68–0.9) along with and spatiotemporal variability. LSTM also performed better in heat wave prediction over the region with similar temporal range (12–36 events) and spatial occurrence as compared to the observation (12–28 events). Overall, the study concludes that LSTM performs better than two methods for Indo‐Gangetic Plain with best hyper parameter tuning. Hence, we propose to utilize a deep learning framework based on LSTM for downscaling GCM dataset at a finer resolution.

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.

Prevalent atmospheric and oceanic signals of the unprecedented heatwaves over the Yangtze River Valley in July–August 2022

Unprecedented heatwaves attacked the Yangtze River valley (YRV) in July–August of 2022. The heatwave-favorable circulation pattern for the YRV is identified by using a self-organizing map. Result shows that the dominant daily circulation pattern driving heatwaves in the YRV is the East Asian anticyclonic pattern (EAAP), characterized by an anomalous deep anticyclone over the East Asian mid-latitudes. Remarkably, the unprecedented frequent occurrence of EAAP in 2022 contributes about 84.6% of the temperature anomalies in the 35 YRV hot days, which is the key dynamical factor for the heatwave in 2022. Physical mechanisms for the occurrence of EAAP are revealed on interannual and interdecadal time scales. On interannual time scale, the dipole sea surface temperature (SST) anomalies over the North Atlantic, characterized by warm and cold SST anomalies in the midlatitudes and subtropics, respectively, exert an important influence on the EAAP days. This dipole SST pattern is unprecedentedly strong in 2022. It facilitates the EAAP through the maintenance of the coupled British–Okhotsk Corridor pattern and the Silk Road pattern in the mid-latitudes. Additionally, the persistent cold SST anomalies in tropical Indian ocean and the central-eastern Pacific from the preceding spring to summer contribute to cooling of the tropical troposphere and induce easterly anomalies in the East Asian subtropics, thereby further promoting the occurrence of EAAP. On interdecadal time scale, the negative Pacific Decadal Oscillation may establish favorable circulation conditions for the occurrence of the EAAP through modulating the interdecadal Silk Road pattern. These findings improve our understanding of the physical mechanisms for driving extreme heatwaves in the YRV and have an implication for enhancing regional heatwave prediction.

Mechanism of the Record-Breaking Heatwave Event Dynamics in South America in January 2022

Heatwaves in the Southern Hemisphere (SH) occur frequently but have received little attention over the years. This study presents a comprehensive analysis of a long-duration, wide-ranging, and high-intensity heatwave event in South America spanning from 9 to 16 January 2022. Before the heatwave occurred, the meridional sea surface temperature (SST) in the SH intensified due to the warming of the South Pacific, while the Southern Annular Mode (SAM) exhibited a positive phase. As a result, the intensified wave activities in the westerlies led to high-pressure anomalies in South America, which played a dominant role in the generation of the heatwave. The diagnostic analysis of thermodynamic equations in South America indicates that the temperature increase during the heatwave was primarily caused by the vertical advection term. In contrast, horizontal advection had a negative impact on surface warming. Additionally, the diabatic heating term associated with surface land types serves as a significant factor that cannot be disregarded. This study aims to deepen our understanding of the mechanisms underlying heatwave generation in South America, enabling the improved prediction of heatwaves and enhanced assessment of potential risks in the future.

Prediction of Heatwave Using Advanced Soft Computing Technique

At present, there is no suitable instrument available to simulate modeling the thermal performance of various areas of our states due to its complicated meteorological behavior. To accurately predict a heatwave, we studied the research gaps and current ongoing research on the prediction of heatwaves. For the accurate prediction of a heatwave, we considered two soft computing concepts, (a) Rough Set Theory (RST) and (b) Support Vector Machine (SVM). All the ongoing research on the prediction of heatwaves is based on future predictions with an error margin. All the available techniques use a particular pattern of heatwave data, and these methods do not apply to vague data. This paper used an innovative RST and SVM technique, which can be applied to vague and imprecise datasets to produce the best outcomes. RST is helpful in finding the most significant attributes that will be alarming in the future. This analysis identifies the heat wave as the most prominent characteristic among various meteorological data. SVM is responsible for the future prediction of heat waves, which includes various parameters. By further classification of heatwaves, we found that a lack of greenery will increase the heatwave in the future. Although the survey was conducted based on a sampling distribution, we expect this result to represent the population as we collected our sample in a heterogeneous environment. These outcomes are validated using a statistical method.

Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data

AbstractHeatwaves lead to catastrophic consequences on public health and the economy. Accurate and timely predictions of regional heatwaves can improve climate preparedness and foster decision‐making to alleviate the burdens due to climate change. In this paper, we propose a heatwave prediction algorithm based on a novel deep learning model, that is, Graph Neural Network (GNN). This new GNN framework can provide real time warnings of the sudden occurrence of regional heatwaves with high accuracy at lower costs of computation and data collection. In addition, its interpretable structure unravels the spatiotemporal patterns of regional heatwaves and helps to enrich our understanding of the general climate dynamics and the causal influences between locations. The proposed GNN framework can be applied for the detection and prediction of other extreme or compound climate events, which calls for future studies.

Global Extreme Heat Forecasting Using Neural Weather Models

Abstract Heatwaves are projected to increase in frequency and severity with global warming. Improved warning systems would help reduce the associated loss of lives, wildfires, power disruptions, and reduction in crop yields. In this work, we explore the potential for deep learning systems trained on historical data to forecast extreme heat on short, medium and subseasonal time scales. To this purpose, we train a set of neural weather models (NWMs) with convolutional architectures to forecast surface temperature anomalies globally, 1 to 28 days ahead, at ∼200-km resolution and on the cubed sphere. The NWMs are trained using the ERA5 reanalysis product and a set of candidate loss functions, including the mean-square error and exponential losses targeting extremes. We find that training models to minimize custom losses tailored to emphasize extremes leads to significant skill improvements in the heatwave prediction task, relative to NWMs trained on the mean-square-error loss. This improvement is accomplished with almost no skill reduction in the general temperature prediction task, and it can be efficiently realized through transfer learning, by retraining NWMs with the custom losses for a few epochs. In addition, we find that the use of a symmetric exponential loss reduces the smoothing of NWM forecasts with lead time. Our best NWM is able to outperform persistence in a regressive sense for all lead times and temperature anomaly thresholds considered, and shows positive regressive skill relative to the ECMWF subseasonal-to-seasonal control forecast after 2 weeks. Significance Statement Heatwaves are projected to become stronger and more frequent as a result of global warming. Accurate forecasting of these events would enable the implementation of effective mitigation strategies. Here we analyze the forecast accuracy of artificial intelligence systems trained on historical surface temperature data to predict extreme heat events globally, 1 to 28 days ahead. We find that artificial intelligence systems trained to focus on extreme temperatures are significantly more accurate at predicting heatwaves than systems trained to minimize errors in surface temperatures and remain equally skillful at predicting moderate temperatures. Furthermore, the extreme-focused systems compete with state-of-the-art physics-based forecast systems in the subseasonal range, while incurring a much lower computational cost.

Spatio-Temporal Generalised Hyperbolic Models with Application to Heatwave Prediction

Spatio-Temporal Generalised Hyperbolic Models with Application to Heatwave Prediction

Structures and Mechanisms of Heatwaves Related to Quasi-Biweekly Variability over Southern China

Abstract In the present study, the structures and mechanisms of the heatwaves (HWs) associated with the quasi-biweekly (QBW; 10–20-day period) variability (QBW-HW) over southern China (SC; 106°–120°E, 21°–30°N) are investigated by using observation data from surface stations in China and the related gridded dataset (CN05.1), and the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis. We found that the strongest anticyclonic anomaly and subsidence appear over SC during the developing phase of QBW-HW, and then induced excess solar radiation at surface and significant diabatic heating lead to a positive surface air temperature change, thus favoring occurrence of QBW-HW over SC. In addition, we found a wet near-surface atmosphere in the QBW-HW events over SC, and further confirmed that near-surface moisture should play an important role in the occurrence of QBW-HW, via absorptions of longwave and shortwave radiation. This result is quite different from previous studies since they did not pay attention to the near-surface moisture. On the other hand, warmer SAT favors more water vapor evaporated from the moist soil when considering the Clausius–Clapeyron relationship. Then, the positive feedback processes promote the occurrence of QBW-HW over SC. In contrast, during the developing and warm phases of QBW-HW over SC, except for the near-surface level, the troposphere is in a dry condition, even at 850 and 700 hPa. In the QBW-HW events over SC, the factor responsible for the wet near-surface atmosphere is the enhanced surface evaporation, which is attributed to strengthened surface wind speed and background moist soil. Significance Statement Under the background of global warming, heatwaves over Southern China are experiencing an increasing trend. In this study, we want to understand the structures and mechanisms of the heatwaves related to 10–20-day (quasi-biweekly) variability. We that found some structures of heatwaves (e.g., anticyclonic anomalies along with subsidence) are consistent with previous studies. In addition, we also show that the moist soil and increased induced near-surface moisture play a key role in the occurrence of heatwaves over Southern China, via enhanced absorptions of longwave and shortwave radiation. This study is helpful for understanding the processes and prediction of heatwaves over Southern China. Future work should examine the findings by some numerical experiments with a climate model.

Prediction and projection of heatwaves

Heatwaves constitute a major threat to human health and ecosystems. Projected increases in heatwave frequency and severity thus lead to the need for prediction to enhance preparedness and minimize adverse impacts. In this Review, we document current capabilities for heatwave prediction at daily to decadal timescales and outline projected changes under anthropogenic warming. Various local and remote drivers and feedbacks influence heatwave development. On daily timescales, extratropical atmospheric blocking and global land–atmosphere coupling are most pertinent, and on subseasonal to seasonal timescales, soil moisture and ocean surface anomalies contribute. Knowledge of these drivers allows heatwaves to be skilfully predicted at daily to weekly lead times. Predictions are challenging beyond timescales of a few weeks, but tendencies for above-average temperatures can be estimated. Further into the future, heatwaves are anticipated to become more frequent, persistent and intense in nearly all inhabited regions, with trends amplified by soil drying in some areas, especially the mid-latitudes. There is also an increased occurrence of humid heatwaves, especially in southern Asia. A better understanding of the relevant drivers and their model representation, including atmospheric dynamics, atmospheric and soil moisture, and surface cover should be prioritized to improve heatwave prediction and projection.

Analysis and intervention of heatwave related economic loss: Comprehensive insights from supply, demand, and public expenditure into the relationship between the influencing factors

Increasing extreme temperatures are producing a serious impact on the economies of cities. However, the importance of social factors is typically neglected by the existing research. In this work, we first establish a supply-demand-public expenditure (SDP) framework for assessing and forecasting heat-related economic loss. Compared with the previous framework, SDP possesses a more comprehensive index system and functions that apply to all types of cities. We selected different economic development and geographical locations (Nanjing, Suzhou, and Yancheng) as case studies to verify the wide applicability of the SDP framework. A qualitative analysis and quantitative prediction of heatwaves and socioeconomic factors on losses were conducted for different cities. The results showed that different loss types displayed obvious regional heterogeneity among the cities. The labor value loss was the most significant type, and health loss was the most vulnerable type. In addition, public expenditure played a neglected critical regulatory role. Apart from these, the current level of public expenditure for heat prevention and control remains insufficient. Based on an assessment of the effects of interventions, policymakers need to make more efforts to increase the proportion of heat-related public spending and ensure stable socio-economic development by utilizing pathways with positive intervention potentials.

Identifying Causes of Short-Range Forecast Errors in Maximum Temperature during Recent Central European Heatwaves Using the ECMWF-IFS Ensemble

Abstract In the last few years, central Europe faced a number of severe, record-breaking heatwaves. Previous studies focused on predictability of heatwaves on medium-range to subseasonal time scales (5–30 days). However, also short-range (3-day) forecasts of maximum temperature (Tmax) can exhibit substantial errors even on larger spatial scales. This study investigates the causes of short-range forecast errors in Tmax over central Europe for the summers of 2015–20 using the 50-member ensemble of the operational ECMWF-IFS (ECMWF-ENS). The 3-day forecast errors, individually calculated for each ensemble member with respect to a 0–18-h control forecast, are fed into a multivariate linear regression model to study the relative importance of different error sources. Outside of heatwaves, errors in Tmax forecasts are predominantly caused by incorrectly predicted downwelling shortwave radiation, mainly due to errors in low cloud cover. During heatwaves, ECMWF-ENS exhibits a systematic underestimation of Tmax (−0.4 K), which is exacerbated under clear-sky and low wind conditions, and other error sources gain importance: the second most important error source is over- or underestimation of nocturnal temperatures in the residual layer. Additional Lagrangian trajectory analysis for the years 2018–20 (due to limited data availability) suggests a link to accumulating errors in near-surface diabatic heating of air masses associated with forecast errors in residence time over land and cloud cover. Regionally, other physical processes can be of dominant importance during heatwaves. Coastal regions are influenced by errors in near-surface wind whereas errors in soil moisture are more important in southeastern parts of central Europe.

An empirical model-based framework for operational monitoring and prediction of heatwaves based on temperature data

An empirical model-based framework for operational monitoring and prediction of heatwaves based on temperature data

Prediction of heat waves using meteorological variables in diverse regions of Iran with advanced machine learning models

Prediction of heat waves using meteorological variables in diverse regions of Iran with advanced machine learning models

Prediction skill of Sahelian heatwaves out to subseasonal lead times and importance of atmospheric tropical modes of variability

Global warming has increased the frequency of extreme weather events, including heatwaves, over recent decades. Heat early warning systems are being set up in many regions as a tool to mitigate their effects. Such systems are not yet implemented in the West African Sahel, partly because of insufficient knowledge on the skill of models to predict them. The present study addresses this gap by examining the skill of the ECMWF ENS extended-range forecasting system (ENS-ext) to predict Sahelian heatwaves out to subseasonal lead-times. It also assesses the importance of tropical modes of variability, which were previously identified as important large-scale drivers of heatwave occurrence in the Sahel. The results show that ENS-ext is able to predict Sahelian heatwaves with significant skill out to lead-week 2–3. With increasing lead-time, heatwaves are more predictable at nighttime than at daytime. Likewise, the pre-monsoon season heatwaves have a longer predictability than those occurring in late winter. The model is also able to relatively well simulate the observed relationship between heatwave occurrence and tropical mode activity. Furthermore, the prediction skill is better during the active phases of the modes, suggesting that they are good sources of heatwave predictability. Therefore, improving the representation of tropical modes in models will positively impact heatwave prediction at the subseasonal scale in the Sahel, and gain more time and precision for anticipatory actions.

Characterizing the Relationship between Temperature and Soil Moisture Extremes and Their Role in the Exacerbation of Heat Waves over the Contiguous United States

AbstractIncreased heat-wave frequency across the United States has led to the need for improved predictability of heat-wave events. A detailed understanding of land–atmosphere interactions and the relationship between soil moisture and temperature extremes could provide useful information for prediction. This study identifies, for many locations, a threshold of soil moisture below which there is an increase in the sensitivity of atmospheric temperature to declining soil moisture. This shift to a hypersensitive regime causes the atmosphere to be more susceptible to atmospherically driven heat-wave conditions. The soil moisture breakpoint where the regime shift occurs is estimated using segmented regression applied to observations and reanalysis data. It is shown that as the soil gets drier, there is a concomitant change in the rate of decrease in latent heat flux and increase in sensible heat flux leading to a strong positive feedback of increased air temperature near the surface, which further dries out the soil. Central, southwestern, and southeastern parts of the United States seem to have regions of clear regime shifts, while the eastern part of the United States generally does not get dry enough to reveal significant breakpoints. Sensible heat flux is seen to be a primary driver of this increased temperature sensitivity aided by the drop in latent heat flux. An investigation of flux tower sites verifies the breakpoint–flux relationships found in reanalysis data. Accurate estimation of these breakpoints can contribute to improved heat-wave prediction.