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The past developments in the predictability of weather and climate are discussed from the point of view of nonlinear dynamical systems. The problems ahead for long‐range predictability extending into the climate time scale are also presented. The sensitive dependence of chaos on initial conditions and the imperfections in the models limit reliable predictability of the instantaneous state of the weather to less than 10 days in present‐day operational forecasts. The existence of slowly varying components such as the sea surface temperature, soil moisture, snow cover, and sea ice may provide basis for predicting certain aspects of climate at long range. The regularly varying nonlinear oscillations, such as the Madden‐Julian Oscillation, monsoon intraseasonal oscillations, and El Niño‐Southern Oscillation, are also possible sources of extended‐range predictability at the climate time scale. A prediction model based on phase space reconstruction has demonstrated that monsoon intraseasonal oscillation can be better predicted at long leads. Key Points The predictability of weather forecast models is limited to less than 10 days because of the limit imposed by chaos and model imperfections The existence of slowly varying components of the climate system and regularly varying phenomena provide basis for climate predictability A prediction model of monsoon intraseasonal oscillation using phase space reconstruction shows that some aspects of climate can be predicted

Seismic nowcasting uses counts of small earthquakes as proxy data to estimate the current dynamical state of an earthquake fault system. The result is an earthquake potential score that characterizes the current state of progress of a defined geographic region through its nominal earthquake “cycle.” The count of small earthquakes since the last large earthquake is the natural time that has elapsed since the last large earthquake (Varotsos et al., 2006, https://doi.org/10.1103/PhysRevE.74.021123). In addition to natural time, earthquake sequences can also be analyzed using Shannon information entropy (“information”), an idea that was pioneered by Shannon (1948, https://doi.org/10.1002/j.1538‐7305.1948.tb01338.x). As a first step to add seismic information entropy into the nowcasting method, we incorporate magnitude information into the natural time counts by using event self‐information. We find in this first application of seismic information entropy that the earthquake potential score values are similar to the values using only natural time. However, other characteristics of earthquake sequences, including the interevent time intervals, or the departure of higher magnitude events from the magnitude‐frequency scaling line, may contain additional information. Plain Language Summary Earthquake nowcasting has been developed to determine the current level of earthquake risk for seismically active areas. Historically, statistical communication theory has been developed to analyze the information content of signals involved in telecommunications and computer systems. We combine these techniques to analyze the information contained in earthquake magnitudes for great earthquakes and mega‐tsunamis. Key Points Nowcasting was previously used to analyze earthquake data to determine current seismic risk to global megacities for magnitudes M ≥ 6 We extend the method to computing hazard from great earthquake and mega‐tsunami sources arising from earthquakes with magnitudes M > 7.9 We also develop nowcasting methods using the information entropy of earthquake sequences

On 6 September 2017, the Sun emitted two significant solar flares (SFs). The first SF, classified X2.2, peaked at 09:10 UT. The second one, X9.3, which is the most intensive SF in the current solar cycle, peaked at 12:02 UT and was accompanied by solar radio emission. In this work, we study ionospheric response to the two X-class SFs and their impact on the Global Navigation Satellite Systems and high-frequency (HF) propagation. In the ionospheric absolute vertical total electron content (TEC), the X2.2 SF caused an overall increase of 2-4 TECU on the dayside. The X9.3 SF produced a sudden increase of 8-10 TECU at midlatitudes and of 15-16 TECU enhancement at low latitudes. These vertical TEC enhancements lasted longer than the duration of the EUV emission. In TEC variations within 2-20 min range, the two SFs provoked sudden increases of 0.2 TECU and 1.3 TECU. Variations in TEC from geostationary and GPS/GLONASS satellites show similar results with TEC derivative of 1.3-1.7 TECU/min for X9.3 and 0.18-0.24 TECU/min for X2.2 in the subsolar region. Further, analysis of the impact of the two SFs on the Global Navigation Satellite Systems-based navigation showed that the SF did not cause losses-of-lock in the GPS, GLONASS, or Galileo systems, while the positioning error increased by 3 times in GPS precise point positioning solution. The two X-class SFs had an impact on HF radio wave propagation causing blackouts at <30 MHz in the subsolar region and <15 MHz in the postmidday sector.

Adverse health outcomes caused by extreme heat represent the most direct human health threat associated with the warming of the Earth's climate. Socioeconomic, demographic, health, land cover, and temperature determinants contribute to heat vulnerability; however, nationwide patterns of residential and race/ethnicity disparities in heat vulnerability in the United States are poorly understood. This study aimed to develop a Heat Vulnerability Index (HVI) for the United States; to assess differences in heat vulnerability across geographies that have experienced historical and/or contemporary forms of marginalization; and to quantify HVI by race/ethnicity. Principal component analysis was used to calculate census tract level HVI scores based on the 2019 population characteristics of the United States. Differences in HVI scores were analyzed across the Home Owners' Loan Corporation (HOLC) “redlining” grades, the Climate and Economic Justice Screening Tool (CEJST) disadvantaged versus non‐disadvantaged communities, and race/ethnicity groups. HVI scores were calculated for 55,267 U.S. census tracts. Mean HVI scores were 17.56, 18.61, 19.45, and 19.93 for HOLC grades “A”–“D,” respectively. CEJST‐defined disadvantaged census tracts had a significantly higher mean HVI score (19.13) than non‐disadvantaged tracts (16.68). The non‐Hispanic African American or Black race/ethnicity group had the highest HVI score (18.51), followed by Hispanic or Latino (18.19). Historically redlined and contemporary CEJST disadvantaged census tracts and communities of color were found to be associated with increased vulnerability to heat. These findings can help promote equitable climate change adaptation policies by informing policymakers about the national distribution of place‐ and race/ethnicity‐based disparities in heat vulnerability. Plain Language Summary As the Earth's climate warms, extreme heat is the most direct threat to human health. Due to various socioeconomic, demographic, health, and environmental factors, some individuals and populations are more vulnerable to adverse health events caused by extreme heat. Publicly available data were obtained for each of these factors, and statistical analysis yielded a quantitative measure of heat vulnerability for 55,267 U.S. census tracts. Of these census tracts, those that have experienced historical and/or contemporary forms of marginalization were associated with increased vulnerability to heat. Additionally, non‐White race/ethnicity groups were associated with increased vulnerability to heat and were overrepresented in the census tracts with the highest vulnerability. These results can inform policymakers of the places and race/ethnicity groups most vulnerable to heat, and can therefore be used to develop equity‐promoting climate change adaptation policies. Key Points Historically “redlined” and contemporary Climate and Economic Justice Screening Tool disadvantaged communities were found to be associated with increased vulnerability to heat Communities of color were associated with increased vulnerability to heat and were overrepresented in the most vulnerable census tracts Identifying place and race/ethnicity‐based disparities in heat vulnerability can help promote equitable climate change adaptation policies

Losses from natural hazards are escalating dramatically, with more properties and critical infrastructure affected each year. Although the magnitude, intensity, and/or frequency of certain hazards has increased, development contributes to this unsustainable trend, as disasters emerge when natural disturbances meet vulnerable assets and populations. To diagnose development patterns leading to increased exposure in the conterminous United States (CONUS), we identified earthquake, flood, hurricane, tornado, and wildfire hazard hotspots, and overlaid them with land use information from the Historical Settlement Data Compilation data set. Our results show that 57% of structures (homes, schools, hospitals, office buildings, etc.) are located in hazard hotspots, which represent only a third of CONUS area, and ∼1.5 million buildings lie in hotspots for two or more hazards. These critical levels of exposure are the legacy of decades of sustained growth and point to our inability, lack of knowledge, or unwillingness to limit development in hazardous zones. Development in these areas is still growing more rapidly than the baseline rates for the nation, portending larger future losses even if the effects of climate change are not considered. Key Points More than half of the structures in the conterminous United States are exposed to potentially devastating natural hazards Growth rates in hazard hotspots exceed the national trend Risk assessments can be improved by considering multiple hazards, mitigation history and fine‐scale data on the built environment

Subseasonal rainfall forecast skill is critical to support preparedness for hydrometeorological extremes. We assess how a process‐informed evaluation, which subsamples forecasting model members based on their ability to represent potential predictors of rainfall, can improve monthly rainfall forecasts within Central America in the following month, using Costa Rica and Guatemala as test cases. We generate a constrained ensemble mean by subsampling 130 members from five dynamic forecasting models in the C3S multimodel ensemble based on their representation of both (a) zonal wind direction and (b) Pacific and Atlantic sea surface temperatures (SSTs), at the time of initialization. Our results show in multiple months and locations increased mean squared error skill by 0.4 and improved detection rates of rainfall extremes. This method is transferrable to other regions driven by slowly‐changing processes. Process‐informed subsampling is successful because it identifies members that fail to represent the entire rainfall distribution when wind/SST error increases. Plain Language Summary Subseasonal rainfall forecasts provide alerts multiple weeks ahead. These forecasts present an opportunity to facilitate anticipatory actions yet are often unreliable to use when preparing for extreme weather. We develop a method to optimize rainfall forecasts by selecting individual members from a large ensemble of dynamic forecasting model outputs based on their ability to represent potential predictors of rainfall. We test our method on monthly rainfall forecasts within Central America in the following month, using Costa Rica and Guatemala as key test cases. We select members from five contributing models of the C3S multimodel ensemble using regional predictors, including wind direction and sea surface temperatures (SSTs). Our results show improvements in the detection of low and high rainfall extremes. This method is transferrable to other regions driven by slowly‐changing processes like SSTs and is beneficial for operational forecasters who can leverage regional expertise of relevant rainfall‐generating processes to subsample better performing ensemble members for their regions. Key Points Subsampling members using sea surface temperatures and zonal wind improves subseasonal ensemble rainfall forecasts in Central America In multiple months and locations mean squared error skill increases by 0.4 and extreme rainfall skill improves by 0.5 (Heidke skill) Process‐informed subsampling is useful because the models' representation of rainfall degrades as process error increases

The Wet Bulb Globe Temperature (WBGT) is an international standard heat index used by the health, industrial, sports, and climate sectors to assess thermal comfort during heat extremes. Observations of its components, the globe and the wet bulb temperature (WBT), are however sparse. Therefore WBGT is difficult to derive, making it common to rely on approximations, such as the ones developed by Liljegren et al. (2008, https://doi.org/10.1080/15459620802310770, WBGTLiljegren${\\mathrm{W}\\mathrm{B}\\mathrm{G}\\mathrm{T}}_{\\mathrm{L}\\mathrm{i}\\mathrm{l}\\mathrm{j}\\mathrm{e}\\mathrm{g}\\mathrm{r}\\mathrm{e}\\mathrm{n}}$ ) and by the American College of Sports Medicine (WBGTACSM87${\\mathrm{W}\\mathrm{B}\\mathrm{G}\\mathrm{T}}_{\\mathrm{A}\\mathrm{C}\\mathrm{S}\\mathrm{M}87}$ ). In this study, a global data set is created by implementing an updated WBGT method using ECMWF ERA5 gridded meteorological variables and is evaluated against existing WBGT methods. The new method, WBGTBrimicombe${\\mathrm{W}\\mathrm{B}\\mathrm{G}\\mathrm{T}}_{\\mathrm{B}\\mathrm{r}\\mathrm{i}\\mathrm{m}\\mathrm{i}\\mathrm{c}\\mathrm{o}\\mathrm{m}\\mathrm{b}\\mathrm{e}}$ , uses globe temperature calculated using mean radiant temperature and is found to be accurate in comparison to WBGTLiljegren${\\mathrm{W}\\mathrm{B}\\mathrm{G}\\mathrm{T}}_{\\mathrm{L}\\mathrm{i}\\mathrm{l}\\mathrm{j}\\mathrm{e}\\mathrm{g}\\mathrm{r}\\mathrm{e}\\mathrm{n}}$across three heatwave case studies. In addition, it is found that WBGTACSM87${\\mathrm{W}\\mathrm{B}\\mathrm{G}\\mathrm{T}}_{\\mathrm{A}\\mathrm{C}\\mathrm{S}\\mathrm{M}87}$is not an adequate approximation of WBGT. Our new method is a candidate for a global forecasting early warning system. Plain Language Summary The Wet Bulb Globe Temperature (WBGT) is an international standard for how we measure the effect of heat on the human body. It is used across sectors in health, industry, sports, and climate to calculate how we feel and how our body responds during heat extremes. Its calculation has historically relied on globe thermometer and wet bulb temperature observations, which are however not widely available. This has made WBGT difficult to calculate and meant approximations have been created. Here we formulate a new WBGT method that can be used with global gridded data that are freely available and we compare it against other methods in common use. We find that our method is accurate when compared to the existing gold standard WBGT method. Key Points We create an accurate method for calculating Wet Bulb Globe Temperature (WBGT) using Mean Radiant Temperature termed WBGTBrimicombe It is found that WBGTamsc87 also known as WBGTsimple is not an accurate approximation of WBGT WBGTBrimicombe can assist with robust heat stress standards across sectors including in public and occupational health

Western North America experienced an unprecedented extreme heat event (EHE) in 2021, characterized by high temperatures and reduced air quality. There were approximately 740 excess deaths during the EHE in the province of British Columbia, making it one of the deadliest weather events in Canadian history. It is important to understand who is at risk of death during EHEs so that appropriate public health interventions can be developed. This study compares 1,614 deaths from 25 June to 02 July 2021 with 6,524 deaths on the same dates from 2012 to 2020 to examine differences in the prevalence of 26 chronic diseases between the two groups. Conditional logistic regression was used to estimate the odds ratio (OR) for each chronic disease, adjusted for age, sex, and all other diseases, and conditioned on geographic area. The OR [95% confidence interval] for schizophrenia among all EHE deaths was 3.07 [2.39, 3.94], and was larger than the ORs for other conditions. Chronic kidney disease and ischemic heart disease were also significantly increased among all EHE deaths, with ORs of 1.36 [1.18, 1.56] and 1.18 [1.00, 1.38], respectively. Chronic diseases associated with EHE mortality were somewhat different for deaths attributed to extreme heat, deaths with an unknown/pending cause, and non‐heat‐related deaths. Schizophrenia was the only condition associated with significantly increased odds of EHE mortality in all three subgroups. These results confirm the role of mental illness in EHE risk and provide further impetus for interventions that target specific groups of high‐risk individuals based on underlying chronic conditions. Plain Language Summary Western North America experienced the most severe extreme heat event (EHE) ever recorded in the region during the summer of 2021. There were approximately 740 more deaths than usual in British Columbia, Canada during the EHE, which made it one of the deadliest weather events in Canadian history. This study compares people who died during the EHE with people who died at the same time of year in other years to identify differences between the two groups with respect to 26 chronic diseases. We found that people with schizophrenia were at much higher risk than others during the EHE. People with chronic kidney disease and ischemic heart disease were also at increased risk. This information will be used to help develop programs that support people at higher risk during future EHEs. Key Points British Columbia experienced an unprecedented extreme heat event (EHE) in summer 2021 associated with a 95% increase in population mortality Deaths during the EHE and previous years were compared with respect to chronic diseases present at time of death Schizophrenia was most strongly associated with higher risk of death during the EHE

How magnetic reconnection is triggered or suppressed is an important outstanding problem. By considering pinching of a current sheet that has formed at non-equilibrium, we show that the background plasma beta is a major controlling factor in the onset and nature of magnetic reconnection. A high plasma beta inhibits a current sheet from pinching down to kinetic scales required for collisionless reconnection, while a low beta facilitates it. A simple adiabatic model provides a good prediction for the reconnection-enabled regions in thickness versus peak plasma beta space, which are confirmed by a series of particle-in-cell simulations with varying initial parameters. A strong dependency of the peak reconnection rate on the plasma beta is clearly predicted with reconnection being favored in low beta conditions. A finite guide field is an additional source of reconnection suppression, consistent with previous observations that reconnection requires a large enough magnetic shear angle for high-beta situations.

The increasing prevalence of warmer trends and climate extremes exacerbate the population's exposure to urban settlements. This work investigated population exposure changes to mean and extreme climate events in different Agro‐Ecological Zones (AEZs) of Pakistan and associated mechanisms (1979−2020). Spatiotemporal trends in mean and extreme temperatures revealed significant warming mainly over northern, northeastern, and southern AEZs. In contrast, mean‐to‐extreme precipitation changes showed non‐uniform patterns with a significant increase in the northeast AEZs. Population exposure to mean (extreme) temperature and precipitation events increased two‐fold during 2000–2020. The AEZs in urban settlements (i.e., Indus Delta, Northern Irrigated Plain, and Barani/Rainfall) show a maximum exposure to extreme temperatures of about 70–100 × 106 (person‐days) in the reference period (1979−1999), which increases to 140–200 × 106 person‐days in the recent period (2000−2020). In addition, the highest exposure to extreme precipitation days also increases to 40–200 × 106 person‐days during 2000–2020 than 1979−1999 (20–100 × 106) person‐days. Relative changes in exposure are large (60%–90%) for the AEZs across northeast Pakistan, justifying the spatial population patterns over these zones. Overall, the observed changes in exposure are primarily attributed to the climate effect (50%) over most AEZs except Northern Irrigated Plain for R10 and R20 events, where the interaction effect takes the lead. The population exposure rapidly increased over major AEZs of Pakistan, which could be more vulnerable to extreme events due to rapid urbanization and population growth in the near future. Plain Language Summary This study investigates the impact of climate change on population in different AEZs of Pakistan from 1979 to 2020. The findings depict widespread warming trends in northern, northeastern, and southern AEZs of Pakistan, whereas changes in precipitation patterns are non‐uniform, with a notable increase in the northeast zone. The study reveals that population exposure to temperature and precipitation events doubled between 2000 and 2020. Urban AEZs such as the Indus Delta, Northern Irrigated Plain, and Barani/Rainfall exhibit the largest exposure to extreme temperatures and precipitation, with exposure increasing over time. The northeast zones of Pakistan experience the highest relative changes in exposure, highlighting the vulnerability of these AEZs to climate events. Climate effect account for the majority of observed exposure changes in AEZs, except Northern Irrigated Plain, where interaction effect plays a key role. The findings suggest that as urbanization and population growth continue, major AEZs in Pakistan are becoming increasingly susceptible to extreme climate events in the future. Key Points Spatiotemporal trends in mean to extreme temperature (precipitation) events reveal widespread warming across Agro‐Ecological Zones Population exposure to mean (extreme) temperature and precipitation events increases two‐fold in recent climate period Observed changes in population exposure are primarily attributed to the climate effect (50%) over major Agro‐Ecological Zones