极端气候变化对哮喘患者全身及气道局部细胞、体液免疫的影响

目的探讨极端气候变化对支气管哮喘（哮喘）患者全身及气道局部细胞、体液免疫的影响。方法将40例哮喘患者均分为两组,对照组用舒利迭干预,观察组用舒利迭＋斯奇康干预;分别于基线点、气温骤降点、气候极冷点检测其血浆CD3＋、CD4＋、CD8＋、CD4＋/CD8＋,血清及诱导痰IgG、IgA、IgM。结果与基线点、气候极冷点比较,两组气温骤降点血浆CD3＋、CD8＋降低,CD4＋、CD4＋/CD8＋升高（P均〈0.01）,血清IgG、IgA、IgM下降（P均〈0.01）。与对照组比较,观察组气温骤降点血浆CD3＋、CD8＋升高,CD4＋、CD4＋/CD8＋降低,血清及诱导痰中IgG、IgA、IgM升高（P均〈0.01）。结论气温骤降可影响哮喘患者的细胞、体液免疫功能,舒利迭＋斯奇康能增强其免疫力,改善气温骤降致机体免疫力下降诱发的哮喘。     

CROWN RATIO AND IRREGULAR STOCKING

Control of mistletoe, a cause of tree decline and death, was investigated in north-eastern Victoria because of reports that mistletoe infestations had increased in recent years. The potential of four herbicides to control mistletoe (Amyema spp.) by injection of the trunks of host eucalypts was evaluated. Eleven eucalypt species were injected in different months in 1984 and 1985 with solutions of the translocatable herbicides GARLON, LONTREL, ROUNDUP and VELPAR.

Evaluation of mistletoe control in conjunction with host health at 12 and 24 months after injection showed that mistletoes could be partially controlled on most species with certain herbicides. The best results were given by ROUNDUP (diluted 1:3 in water) or GARLON (1:4) injected at the rate of 1 mL per cut, with cuts spaced at 10 cm intervals around the lower trunk of the host tree. LONTREL and VELPAR gave unsatisfactory results, though they received only limited testing.

The particular herbicide and month of injection that gave the greatest percentage control of mistletoes with no or limited host mortality varied according to the eucalypt species, although the most effective months were commonly January and June. Smaller host trees (< 30 cm diameter) were significantly more susceptible to death following injection than larger trees.

This study has established that host injection with certain herbicides is a feasible and effective technique for reducing Amyema spp. populations on many eucalypt species. The results should be applicable to the 11 studied host eucalypt species infested with Amyema spp. growing elsewhere under similar conditions, but extrapolation to other species or genera (of host or mistletoe) would be inadvisable without further tests.     

Increases in Neuronal Activity in Rat Spinal Trigeminal Nucleus Following Changes in Barometric Pressure—Relevance for Weather‐Associated Headaches?

Objective.— To determine whether controlled changes in barometric pressure activate rat spinal trigeminal neurons as a possible animal correlate of headaches. Background.— Changes in weather accompanied by changes in atmospheric pressure are suggested to trigger primary headaches. Mechanisms that increase neuronal activity in the rat spinal trigeminal nucleus may parallel those that contribute to the generation of headaches. Methods.— Urethane anesthetized rats were placed in a climatic chamber, in which the air pressure could be selectively manipulated. The parietal cranial dura mater and the spinal dura mater covering the medulla were exposed. Electrolyte‐filled electrodes were introduced into the spinal trigeminal nucleus to record from neurons with receptive fields in facial areas and the cranial dura mater and/or the cornea and/or the temporal muscle. Arterial pressure and heart rate were monitored. The barometric pressure was lowered by 40 hPa during 8 minutes, kept at this level for 8 minutes and returned to the previous level. Results.— During lowering of the barometric pressure and the low pressure period a sample of neurons showed increased discharge rates. Group analysis revealed that it was the group of units with receptive fields in the cornea, but not in the dura mater or the temporal muscle, which was significantly activated when the animal was exposed to low atmospheric pressure. Exposure of the cranial dura and opening of the cisterna magna did not prevent an increase in activity. In another sample of units the activity recorded after infusion of the nitric oxide donor sodium nitroprusside did not change under low pressure exposure. Arterial pressure and heart rate changed slightly during barometric pressure changes. Conclusions.— We conclude that distinct neurons in the trigeminal nucleus caudalis, particularly with preferential afferent input from the eye, respond to lowering of atmospheric pressure. Similar mechanisms may contribute to the generation of headaches during changes in weather.     

Policy and weather influences on mobility during the early US COVID-19 pandemic

As the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to proliferate across the globe, it is a struggle to predict and prevent its spread. The successes of mobility interventions demonstrate how policies can help limit the person-to-person interactions that are essential to infection. With significant community spread, experts predict this virus will continue to be a threat until safe and effective vaccines have been developed and widely deployed. We aim to understand mobility changes during the first major quarantine period in the United States, measured via mobile device tracking, by assessing how people changed their behavior in response to policies and to weather. Here, we show that consistent national messaging was associated with consistent national behavioral change, regardless of local policy. Furthermore, although human behavior did vary with outdoor air temperature, these variations were not associated with variations in a proxy for the rate of encounters between people. The independence of encounters and temperatures suggests that weather-related behavioral changes will, in many cases, be of limited relevance for SARS-CoV-2 transmission dynamics. Both of these results are encouraging for the potential of clear national messaging to help contain any future pandemics, and possibly to help contain COVID-19.

On March 13, 2020, US President Donald Trump announced a state of emergency and a ban on travel from 26 European countries (1). Soon thereafter, a national stay-at-home guideline was issued on March 16 (2). Every state announced school closures between March 16 and March 23 (3), rendering March 21−22 the first weekend within this school and workplace closure period. Since response to the virus in the United States has been widely politicized (4, 5), we examine how human behavior, reflected through mobility changes, responded to policies that aimed to limit person-to-person interactions (6). Because COVID-19 will remain dangerous until safe and effective vaccines are widely distributed (7), mobility interventions are crucial and have been successful in other countries (8). Fig. 1A shows the timing of statewide policies and of a variety of mobility changes. As a proxy for the number of people who may have come face to face, potential encounter rate is a mobility metric, measuring the number of devices that come within 50 m of each other (9) (see more discussion in Materials and Methods). We compute the mobility changes by identifying change points in the potential encounter rate time series from February 24 (the start of data availability) to May 22. Change points are identified by locating the times of greatest change and finding the nearest local minima (see Materials and Methods). The grocery visitation maxima (Fig. 1A, yellow) are derived from the grocery and pharmacy visits of the Google Community Mobility Reports (10), while all other mobility metrics are calculated from Unacast (11) potential person-to-person encounter rates.Open in a separate windowFig. 1.(A) Implementation of state policies and changes in mobility behavior, ordered by date of stay-at-home orders. Warm-colored circles are metrics based on mobility: peaks in grocery visitation (yellow), beginning of quarantine based on mobility (red), timing when mobility reaches 30% of prepandemic values (orange), and the end of quarantine based on mobility (pink). Cool-colored squares are policy implementations: date of implementation of stay-at-home for each state (blue), date of expiration of stay-at-home (cyan), and date of implementation of reopening plans (green). Black lines are national declarations: the announcement of a national state of emergency (dashed), and the start and end of national stay-at-home guidelines (solid). For details about mobility metrics, see Materials and Methods. (B) Bar graph of county-level changes in mobility behavior, using real quarantine time series (red, pink) and control time series (blue, cyan).Within a few days of the March 13 presidential announcement, every state in the nation had a peak in trips to the grocery store and pharmacy (Fig. 1A, yellow). Following the issuance of a national stay-at-home guideline and school closures, almost all states achieved their maximum decrease in mobility (Fig. 1A, red) on Saturday March 21, marking the effective beginning of a stay-at-home period nationwide. Although many states delayed implementing stay-at-home orders, there was near uniformity in the beginning and ending of quarantine behavior across states. The distinction between the nationally coherent timing of the grocery peak (yellow) and encounter decrease (red) and the scattershot timing of when state policies were put into place (Fig. 1A, blue) is striking. Again, we note that there were numerous factors at play at this time, with school closings being particularly important. Therefore, this consistency in timing of the quarantine start does not necessarily relate directly to the national guidance. Indeed, inspection of mobility time series for individual states suggests that, in many states, mobility was beginning to decline prior to the national state of emergency and stay-at-home guidelines.On May 1, US national stay-at-home guidelines expired (12), but schools and many workplaces remained closed (3). Nevertheless, there was nationally coherent timing of potential encounter increases (Fig. 1A, pink), in contrast to the varied timings of state stay-at-home order expirations (Fig. 1A, cyan) and of state reopening plan implementations (Fig. 1A, green). To further examine the national coherence of the potential encounter decrease (red) and potential encounter increase (pink), we use county-level Unacast potential encounter data. Out of 3,054 counties for which Unacast had data, 1,881 counties had March 21 as the beginning of quarantine behavior, and a total of 2,536 counties had its beginning in the 3-d span of March 21−23, comprising 83% of the total number of counties; 1,431 counties had May 2 as the end of quarantine behavior, and a total of 2,553 counties had April 30 to May 2, a span of 3 d, also comprising 84% of total counties. As a consistency check, similar results were obtained using the county-level Cuebiq Mobility Index (13), a metric of distance traveled by mobile devices.To determine whether this degree of spatial coherence in mobility changes is unusual for the United States, we employ the same algorithm on a subsequent encounter rate time series of equal length (June 1 to August 28) to test whether a similar consistency is observed for any other dates. We find that the most common date selected from these control series as the “beginning of quarantine behavior” was June 29, with a frequency of only 638 counties. In the 3-d span June 28−30, only 26% of counties “began quarantine,” substantially fewer than during the actual quarantine beginning on March 21−22. Similarly, the most common date designated as “end of quarantine behavior” was August 6, with a frequency of only 766 counties and with 38% of counties included in the surrounding 3-d span.These findings are illustrated in Fig. 1B, showing that the state-level coherence of quarantine start and stop demonstrated in Fig. 1A is visible even at the county level. The distributions of the quarantine start and stop points in the spring do not overlap and are quite sharply peaked, while the mobility change point distributions computed from the summer control series are broader and overlap.While the timings of the dramatic encounter decreases and increases are consistent, many states had already reached encounter rates that were 30% of their prepandemic values (Fig. 1A, orange) before quarantine behaviors ceased (Fig. 1A, pink), suggesting variability across states in people’s behaviors and encounters during the quarantine period. Most of these states experienced a new surge in COVID-19 cases in July (14), consistent with studies that argue that an early reopening led to this new wave (15, 16). Dates of reaching 30% of prepandemic potential encounter rate are not correlated with implementation dates of reopening plans (R² = 0.062) or with expirations of stay-at-home orders (R² = 0.049), which is further evidence that mobility behaviors are substantially independent of state policies. The consistency across mobility measures suggests the primacy of national awareness and national guidelines over state policies in determining human behavior.     

Dust concentration around the sites of demolition work after the Great Hanshin-Awaji Earthquake

The total dust concentration and the particle size distribution were determined around die sites of demolition associated with the Great Hanshin-Awaji Earthquake, which occurred on January 17, 1995. The total dust concentrations ranged from 0.20 to 0.23 mg/m³, being about 1.2 to 2.2 times that in die non-demolition area, and intermediate particles (2.1-11.0μm) made up a large proportion of the dust. The dust concentrations were not influenced by the weather on the day preceding measurement around the sites of demolition of concrete buildings, whereas the values decreased to about half around die sites of demolition of wooden buildings, nearly the same concentration in the control areas, when it had rained on the previous day. The dust concentrations increased compared with that in an average year but to The degree of die upper limit of die environmental standard (1 hr-value<0.20 mg/m³) . The dust due to the smoke of Mt. Sakurajima in the surrounding areas accounted for a higher proportion of large particles (<11.0>m) than in the earthquake-devastated area. The concentration of respirable dust (<;7.07>m) in a worker engaged in demolition was 4.0 mg/m³, being twice the recommended concentration (2 mg/m³) of the Japan Society for Occupational Health. It was thus considered that workers should use a respiratory protective device.     

Influence of climate factors on emergency visits for childhood asthma attack

BACKGROUND: Asthma attack shows strong seasonality. The purpose of the present study was to quantify the contribution of climate variables and other seasonal factors on the incidence of emergency visits for childhood asthma in Tokyo, Japan. METHODS: The number of children who visited emergency rooms at Jikei university hospitals in Tokyo during 1998-2002 (5559 visits) was retrieved retrospectively from files from the Department of Pediatrics, and compared with 45 climate parameters from the Meteorological Agency using multiple regression models with a stepwise backward elimination approach. RESULTS: The number of visits (3.7 +/- 3.1) per night increased significantly when climate conditions showed a rapid decrease from higher barometric pressure, from higher air temperature and from higher humidity, as well as lower wind speed. The best-fit model demonstrated that a 22% variation in the number of visits was explained by a linear relationship with 12 climate variables, which increased to 36% after adjusting for calendar month and day of the week. Moreover, when the number of asthma visits was cut off at nine per night, the area under the receiver operator characteristics curve was 0.91 (95% CI: 0.89-0.94) in the multiple logistic regression model using the same variables. CONCLUSIONS: These results suggest that these models might quantify contributions of specific climate conditions and other seasonal factors on the number of emergency visits per night for childhood asthma attack in Tokyo, Japan.     

From the Cover: Quantifying contributions of natural variability and anthropogenic forcings on increased fire weather risk over the western United States

Previous studies have identified a recent increase in wildfire activity in the western United States (WUS). However, the extent to which this trend is due to weather pattern changes dominated by natural variability versus anthropogenic warming has been unclear. Using an ensemble constructed flow analogue approach, we have employed observations to estimate vapor pressure deficit (VPD), the leading meteorological variable that controls wildfires, associated with different atmospheric circulation patterns. Our results show that for the period 1979 to 2020, variation in the atmospheric circulation explains, on average, only 32% of the observed VPD trend of 0.48 ± 0.25 hPa/decade (95% CI) over the WUS during the warm season (May to September). The remaining 68% of the upward VPD trend is likely due to anthropogenic warming. The ensemble simulations of climate models participating in the sixth phase of the Coupled Model Intercomparison Project suggest that anthropogenic forcing explains an even larger fraction of the observed VPD trend (88%) for the same period and region. These models and observational estimates likely provide a lower and an upper bound on the true impact of anthropogenic warming on the VPD trend over the WUS. During August 2020, when the August Complex “Gigafire” occurred in the WUS, anthropogenic warming likely explains 50% of the unprecedented high VPD anomalies.

The western United States (WUS) is prone to large wildfires, over 90% of which occur in the warm season (May to September) according to the Monitoring Trends in Burn Severity (MTBS) database (1). The year 2020 was a record-breaking fire season in the history of the WUS, especially in the coastal states of California, Oregon, and Washington. Many recent studies of fire behavior in the WUS have indicated warm season increases in the area burned by fires, fire frequency and intensity, and fire season length (2–12). Analysis of the MTBS data in Fig. 1 A and B shows that the average warm season burned area in the WUS during 2001 to 2018 was about 3.35 million acres, nearly double (+98%) that of the previous period of 1984 to 2000 (1.69 million acres). According to the National Interagency Fire Center (NIFC) report, the area burned by wildfire during the 2020 warm season reached 8.8 million acres (13, 14), more than five times the average during 1984 to 2000. This rapid increase in burned area has been observed across most of the WUS except in Wyoming. It has been linked to more extreme fire weather risk, largely due to high vapor pressure deficit (VPD) (10, 15, 16). In the warm season, the number of days per year with high VPD (defined as days with VPD larger than the 90th percentile value of VPD in the climatological period of 1979 to 2010) increased by 94% during 2001 to 2018 relative to 1984 to 2000 (Fig. 1 C and D).Open in a separate windowFig. 1.(A) Annual mean burned areas (10⁵ acres/yr) in the warm season during the period 1984 to 2000. Results for the average of each state are given by shading and with a numerical value. The averaged burned areas over the whole WUS are shown in the Lower Left corners. (B) Same as A but for the period of 2001 to 2018. The percentage changes of burned areas relative to those of the 1984 to 2000 period are shown below the annual mean burned areas. (C) Average days with high VPD (percentile VPD′ over 90% in a year) for the 1984 to 2000 period. (D) Same as B but for the averaged days with high VPD.Many factors and their complex interactions can contribute to increased fire activity. In addition to an increase in VPD or evaporative demand due to warming, fire behavior is also affected by ignition sources (17), forest management (18), tree mortality from bark beetles (19), earlier and reduced springtime snowmelt (7), reduced summer precipitation (20), cloud shading (21), vegetation cover (22), fog frequency (23), live fuel moisture content (23–25), and increase in fire-prone wind patterns (26, 27). Recently, there has been intense interest in the issue of how anthropogenic warming may impact fire behavior. Several studies have used climate model simulations to assess the impact of anthropogenic forcing on increased fire activity in the WUS (10) and in other regions (28, 29). The contribution of anthropogenic climate change is often estimated by the linear trend or long-term low-pass filtered time series of the fire indices. The contribution of atmospheric internal variability is approximated by considering the detrended fire index time series (16, 30) or by comparing historical simulations with realistic anthropogenic forcings to simulations with natural climate forcings only (28).The VPD at synoptic to decadal time scales is closely related to atmospheric circulation patterns (31–34). Winds from hot inland areas and subsidence associated with high surface pressure systems generate hot and dry air, leading to high VPD values. However, few studies have evaluated the influence of natural internal climate variability on multidecadal changes in VPD. This is in part because of the difficulty of partitioning observed temporal variations into internally generated and externally forced components (35). Partitioning these components is more straightforward in climate models. Large initial condition ensembles are particularly useful for this purpose (36). One problem, however, is that many models may inadequately represent regional patterns of internal variability, especially over the WUS (37). Using climate model simulations to estimate the impact of natural variability of the atmospheric circulation on VPD is therefore challenging and subject to large uncertainties.As a consequence, it has been unclear whether the observed change in VPD since 1979 exceeds the VPD change that can be explained by internal variability alone. To address this issue, and to better quantify the relative contributions of internal variability and external forcing (particularly anthropogenic forcing) to the observed increase in fire weather in the WUS, we consider an observation-based flow analogue approach (38–40). This approach characterizes VPD values based on their distribution for a given atmospheric circulation pattern (e.g., geopotential height at 500 hPa, Z500) constructed from a suite of similar circulation patterns during a climatological period (e.g., 1979 to 2010).Different flow analogue approaches have been reported in the literature. Our analysis shows that the choice of approach and the choice of observational dataset (the reanalyses listed in SI Appendix, Table S1) can affect our flow analogue estimates. This is why we introduce an ensemble constructed flow analogue scheme. In this approach, multiple analogue schemes are constructed. Their interquartile range (IQR) is used to account for uncertainties in analogue VPD estimates arising from the choice of approach and observational dataset (see Methods). In addition, we also evaluate VPD trends in multimodel ensembles of simulations provided by the sixth phase of the Coupled Model Intercomparison Project (CMIP6; reference SI Appendix, Table S2). Our analysis of CMIP6 simulations yields a model-based estimate of the forced component of VPD changes, thus providing an independent check on our observational attribution of VPD trends.Historical Trends of Fire Weather Risk.How has the WUS warm season fire weather risk (as represented by VPD) increased since the beginning of the satellite era in 1979? Previous studies have already shown an increase of VPD over a large area of the United States (15, 16, 32). For example, Abatzoglou and Williams (10) estimated the VPD trend over 1979 to 2015 to be 1.73 σ per 37 y (0.47 σ/decade). Here, we extend the analysis period from 2015 to 2020 using gridded surface meteorological (gridMET) (41) observations and show the linear trend in the time series of warm season mean VPD anomaly for the WUS (VPD′; Methods). Fig. 2A indicates that the warm season mean VPD′ over the WUS has increased significantly (P < 0.01) by 0.48 ± 0.25 hPa/decade (95% CI). After normalizing the trend by the SD (σ = 0.93 hPa) of the detrended VPD (see Methods) during the climatological period of 1979 to 2010, it is equivalent to 0.52 ± 0.27 σ/decade. This trend is close to the previously estimated VPD trend during 1979 to 2015 (10). The trend of increasing VPD is significant across most of the WUS, except for the northeastern WUS and part of Washington state (Fig. 2D). Further analysis reveals that these VPD trends are generally robust to different choices of method used for estimating the slope of a regression line (SI Appendix, Table S3).Open in a separate windowFig. 2.(A) Average time series of VPD′ from the gridMET dataset (solid line) and burned areas from the MTBS dataset (bars) for all warm season days. The VPD′ trend is the slope of the regressed line (dashed line) of the time series for all available years (1979 to 2020). The VPD′ trend for the shorter period 1984 to 2018 shows a similar result (SI Appendix, Fig. S1). (B and C) Same as A but for time series of e_s and e_a. (D–F) Trend map of these anomalies for the WUS (all warm season days). The absence of hatching denotes regions where the trends are significant at the P < 0.05 level.Fig. 2A also shows that the burned area in the warm season generally follows both the VPD trend and the variations in VPD on interannual to decadal time scales. The correlation coefficient between the burned area and VPD′ time series is 0.73 (P < 0.01). This indicates that VPD is the leading climatic control on the burned area over the WUS. Strong functional relationships between VPD and the burned area have been found in United States and other regions of the world (10, 15, 16, 28, 42). VPD′ associated with large fire events, defined by VPD′ averaged within the areas and during the days of the large fires, are systematically higher than those of all warm season days by about 3 hPa on average (SI Appendix, Fig. S1). The former shows a similar increase trend to the latter.To quantify the contributions of surface warming and drying to the VPD trend over 1979 to 2020, we evaluate the time series of saturated vapor pressure (e_s; Fig. 2B) and actual vapor pressure (e_a; Fig. 2C) of the surface air. Fig. 2B shows a significant (P < 0.01) trend of increasing e_s at a rate of 0.40 ± 0.20 hPa/decade (0.50 ± 0.25 σ/decade). In contrast, e_a decreases but does not show a significant negative trend (P = 0.12; Fig. 2C). Overall, the increase in e_s explains 82% of the total VPD trend, indicating that the increase in VPD over the WUS is largely due to warming (increase of e_s; Fig. 2 B and E). This is generally consistent with the findings of previous studies (10). The spatial distributions of the e_s and VPD trends are very similar (Fig. 2 D and E); the drying effect represented by the decrease of e_a accounts for 18% of the trend (Fig. 2C) and is only significant over parts of California, Nevada, and the Southwest (Fig. 2F).Contribution of Atmospheric Circulation Changes and Anthropogenic Warming to Increasing Fire Weather Risk. The relationship between hot and dry conditions and large-scale atmospheric circulation is well known. For example, Crimmins (31) found that 80% of the extreme fire weather days during late spring to early summer in the US Southwest were linked to the southwesterlies and anomalous high pressure systems over that region. While such a general characterization captures the averaged anomalous atmospheric circulation pattern associated with high VPD (SI Appendix, Fig. S2), there is a significant variation in the location, shape, and strength of the anomalous high associated with high VPD in different states of the WUS (SI Appendix, Fig. S3).To quantify the contribution of the atmospheric circulation changes to the observed changes of VPD′, we apply an ensemble constructed flow analogue method modified from previous flow analogue or dynamical adjustment approaches (38, 43). For simplicity, this method is referred to as the analogue method, and the estimated VPD′ associated with atmospheric circulation is hereafter referred to as the analogue VPD′. Full details are provided in the Methods section. We then determine the fraction of the observed increase in VPD in recent decades can be explained by a more frequent occurrence of circulation patterns that favor high VPD, that is, by the analogue VPD′. The underlying assumption here is that any change in the frequency of a “high VPD” atmospheric circulation pattern is due to internal variability alone. If the VPD associated with a specific circulation pattern is systematically higher in recent decades than in the past, then such systematic increases in VPD are likely due to anthropogenic warming and associated thermodynamic feedbacks, particularly if they are consistent with the VPD changes simulated by global climate models in response to anthropogenic forcing.Our results show that the daily analogue VPD′ explains a large fraction of the total variance of the observed VPD′ averaged over the WUS for all warm season days during 1979 to 2020 (R² = 77%), indicating that the analogue method successfully captures the influence of synoptic variations in circulation patterns on VPD.Fig. 3A shows the time series of the observed VPD′ compared to that of the VPD′ expected from the atmospheric circulation (i.e., the analogue VPD′) during the warm season of 2020. The 2020 warm season started with relatively mild weather conditions from May to early July. The observed VPD′ closely matches the analogue VPD′ during this period, suggesting that the observed VPD′ was mainly influenced by the variation of the atmospheric circulation. After early July, however, the observed VPD′ is higher than the analogue VPD′. This difference between observed VPD′ and analogue VPD′ is especially pronounced for the two extreme VPD′ spikes during the August Complex “Gigafire” (mid-August) and the California Creek fire (early September). These two fires were ranked No. 1 and No. 5 in California wildfire history at the time of writing (44). For August 2020 (Fig. 3B), the probability density function (PDF) of the observed VPD′ showed a strong shift toward high VPD′ relative to both its climatology and analogue VPD′. The mean value of the observed VPD′ in August 2020 was 4.9 hPa (∼2.1 σ) higher than that of the August climatology; the mean analogue VPD′ in August 2020 exceeded the climatological mean of the observed VPD′ by 2.3 hPa (∼1.0 σ). We conclude from this that the strong anomalous circulation condition can only explain about half of the exceptionally high VPD′ in August 2020.Open in a separate windowFig. 3.(A) VPD′ time series in 2020 warm season over the WUS from both observations (black line) and analogues (blue line for mean analogue; shading for IQR). Starting days of the August Complex fire and California Creek fire are labeled. Dashed horizontal lines are the warm season mean values. (B) PDF of August VPD′ for the observations from the climatological period of 1979 to 2010 (black curve), 2020 observations (red bars, shaded dark gray where they overlap with blue bars), and 2020 analogues (blue bars). The three vertical lines in each box plot represent the 25th, 50th, and 75th percentiles, the dot represents the mean value, and the whiskers extend to two SDs from the mean. (C) Map of Z500 (contours) and its standardized anomalies relative to 1979 to 2010 climatology (shading) averaged over four reanalysis datasets (the fifth generation of the European Centre for Medium-Range Weather Forecasts [ECMWF] atmospheric reanalysis [ERA5], the Modern Era Retrospective analysis for Research and Applications version 2 [MERRA-2], the National Centers for Environmental Prediction [NCEP] Climate Forecast System Reanalysis [CFSR], and the Japanese 55-y Reanalysis [JRA55]) on August 16, 2020, the start date of the August Complex fire. (D) Percentile VPD map on the same date as C, overlaid with the 95, 99, and 100% contours. (E) Same as D but for constructed analogue VPD map.On August 16, 2020 when the August Complex fire started, an extensive and strong anomalous high was centered over the Southwest and dominated the whole WUS (Fig. 3C); most values of VPD′ across the WUS ranked in the 99th or even the 100th percentile—that is, they were equal to or exceeded maximum VPD′ values observed in the same region (within a 31-d period centered on August 16) during the climatological period of 1979 to 2010 (Fig. 3D). While the analogue VPD′ on this day also show very high VPD conditions over the whole WUS, they were less extreme than the observed VPD′ (Fig. 3E; note that there are no contours of the 99th and 100th percentiles). In fact, averaged over the WUS, the analogue VPD′ could only account for ∼68% of the observed VPD′ for the August 16 event and even less (∼48%) for the September 4 event (Fig. 3A). Thus, the observed high VPD′ values during the 2020 warm fire season significantly exceeded VPD′ values that can be explained by the atmospheric circulation pattern.On the interannual time scale, Fig. 4A shows that the analogue and observed warm season mean VPD′ time series display very similar variations (R² = 68%). Since 2000, however, the observed VPD′ was systematically higher than the analogue VPD′. The trend of analogue warm season mean VPD′ is 0.15 ± 0.15 hPa/decade, explaining 32% of the observed VPD trend (0.48 ± 0.25 hPa/decade); the IQR of these trends for all 180 different analogue schemes (see Methods) is 0.13 to 0.20 hPa/decade, explaining 27 to 42% of the observed VPD trend. The residual VPD trend (observed minus analogue) is 0.33 ± 0.16 hPa/decade, explaining 68% of the observed trend; the IQR of all 180 residual trends is 0.30 to 0.36 hPa/decade, explaining 62 to 75% of the observed VPD trend.Open in a separate windowFig. 4.(A) Warm season mean VPD′ time series over the WUS from observations (black line), analogues (blue line; shading represents IQR for VPD′ from the 180 analogue schemes described in Methods), and residuals (observations minus analogue, red line, shading represents IQR). (B) PDF of the residual VPD anomalies for the periods 1979 to 2000 and 2001 to 2020, respectively, and box plots (see Fig. 3B for explanation). (C) Analogue VPD′ trend (1979 to 2020) in each state. The value shown by bold black font within each state shows the VPD trend of that state. The value shown by bold black font in the Lower Left corner is the VPD trend averaged over the entire WUS. One, two, or three asterisk(s) next to these trend numbers denotes trend significance at P < 0.1, 0.05, and 0.01, respectively. Numbers inside brackets are IQR of the trends calculated from 180 individual analogue schemes. (D) Same as C but for residual VPD′ trend (observations minus analogue). (E) Percentage of the analogue VPD trend relative to the observed VPD trend (IQR in brackets). Montana, Wyoming, and Washington have nonsignificant observed VPD trends at the P < 0.05 level (SI Appendix, Table S4), and the corresponding regions are therefore hatched in C–E.Fig. 4B shows the PDF of residual VPD′. The PDF curve is basically symmetric about zero during the first two decades of our analysis period (1979 to 2000), suggesting a dominant influence of random variability of the atmospheric circulation on VPD. During the recent two decades (2001 to 2020), the mean residual VPD′ shifted to the positive side by 1.00 hPa (0.54 σ) relative to the period of 1979 to 2000. This is primarily due to a shift of +1.37 hPa (0.53 σ) in the mean observed VPD′; the shift in the mean of the analogue VPD′ (+0.38 hPa or 0.14 σ) is less than a third of that observed (SI Appendix, Fig. S4).Fig. 4 C and D show the analogue and residual VPD′ trends averaged over each state in the WUS. Similar to the result for the entire WUS, most states (especially those with significant observed VPD′ trends; Fig. 1 and SI Appendix, Table S4) have an analogue trend that is considerably smaller than the residual trend. The trend ratio (analogue to observed) in Fig. 4E suggests that for the eight states with significantly positive trends for the observed VPD′, the circulation contribution ranged from 24% (Idaho) to 39% (Utah), leaving 76 to 61%, respectively, of the residual trend unexplained. Overall, these results indicate the analogue VPD′ trend associated with circulation changes can only explain about one-third of the observed VPD trend across most of the WUS.This residual VPD′ mainly represents the thermodynamically contributed VPD′ after removing the dynamically controlled analogue VPD′. It is contributed by both thermodynamic feedbacks to the natural circulation changes, such as land surface feedbacks, and warming due to anthropogenic forcing. The relatively small residual VPD′ values prior to 2000 are presumably dominated by the thermodynamic feedbacks, whereas the systematic increase of the residual VPD′ afterward are likely contributed by anthropogenic forcings and the associated thermodynamic feedbacks.In addition to atmospheric circulation changes, could reduced cloudiness and vegetation cover contribute to the increases of VPD? Such changes would enhance solar radiation and evaporative demand, resulting in warmer and drier conditions, and thus higher in VPD (21, 45). We note, however, that an increase in downward surface solar radiation is mostly confined in the coastal states (California, Oregon, and Washington) and to northern Idaho and southwestern Arizona, whereas the decreases of Normalized Difference Vegetation Index (NDVI) are confined to Southern California and southwestern Arizona (SI Appendix, Fig. S5 C and D). These changes cannot explain a widespread increase of VPD′ and residual VPD′ across the entire WUS (SI Appendix, Fig. S5 A and B). Only southwestern Arizona exhibits both an increase in downward solar radiation and reduced NDVI; in this particular region, therefore, trends in both factors could contribute to the strong increase in observed VPD′.The previous discussion and figures focused solely on the observations. We attempted to partition observed VPD trends into a component associated with circulation changes and a residual component likely to be dominated by the response of VPD to external forcing. In the following, we consider VPD trends in the CMIP6 models. Fig. 5A compares the VPD′ trend between models and observations over the same 1979 to 2020 period. Simulations with combined natural and anthropogenic forcings (which comprise historical simulations up to 2014 and the Shared Socioeconomic Pathway 5 - Representative Concentration Pathway 8.5 [SSP5-8.5] scenario integrations thereafter) show a significant (P < 0.01) warm season mean VPD trend of 0.48 ± 0.05 hPa/decade (Fig. 5 A and C), which is very similar to the observed VPD trend over the WUS.Open in a separate windowFig. 5.(A) Warm season mean VPD′ time series averaged over the WUS region and the trends during 1979 to 2020 calculated with climate models and observations (daily gridMET and monthly PRISM). The orange and blue line represents observed and residual VPD′ from gridMET, respectively; the yellow line represents observation from PRISM (a longer term monthly observational dataset covering 1895 to present that gridMET is based on); the black and cyan solid lines represent CMIP6-ALL and CMIP6-NAT simulations, and the thin gray and cyan lines are for all ensemble members from CMIP6-ALL and CMIP6-NAT, respectively. For the purposes of visual display, the VPD′ lines for ALL, NAT, and PRISM are forced to have the same mean value during 1979 to 2010 as gridMET. The VPD trends, 95% CI, and IQR (only for residual VPD′) labeled in the Upper Left corner are calculated for the 1979 to 2020 period. (B) PDF of VPD trend for PRISM observations and CMIP6-NAT; the vertical lines, dots, and whiskers for the box plots are defined as in Fig. 3B; VPD trend is calculated for every consecutive 42-y period within the periods listed above. (C) Same as B but for CMIP6-ALL. When calculating the ensemble-mean VPD trends and their PDFs in the CMIP6 simulations, VPD trend is first calculated for each ensemble member of each model, and weights are given to all the members in a way that all members from the same model are equally weighted and all models are also equally weighted.In contrast, historical runs with only natural solar and volcanic external forcings show a very small mean VPD trend of 0.06 ± 0.05 hPa/decade (P < 0.05), with a middle 99% range from −0.37 to 0.46 hPa/decade (Fig. 5B). The observed VPD trend exceeds over 99% of the trend values that can be explained by natural climate forcings (solar and volcanic) and internal variability. Our natural variability estimates are based on a large number (∼15,000) of 42-y trend samples from 14 different climate models. The observed VPD trend is very similar to the mean of the modeled trends in the CMIP6 simulations with combined natural and anthropogenic external forcings. Over 1979 to 2020, the PDF of model VPD trends under “all forcings” spans the range from 0 to 1 hPa/decade (Fig. 5C). This range arises from natural climate variability, from model differences in historical external forcing, and from model differences in the response to forcing.The difference between the multimodel ensemble with combined anthropogenic and natural forcings and the multimodel ensemble with natural forcing only—which we refer to as “ALL” and “NAT” hereafter—is widely used for estimating the anthropogenically forced component of climate change (28, 46–49). Here, differencing the means of the ALL and NAT multimodel ensembles yields an anthropogenically forced VPD trend of 0.42 hPa/decade, equivalent to 88% of the observed VPD trend over the period of 1979 to 2020. This is roughly 30% larger than our observationally derived residual VPD′ trend of 0.33 ± 0.16 hPa/decade.     

The Effect of Weather Variability on Pediatric Asthma Admissions in Athens, Greece

The aim of this study was to determine whether there is any association between weather variability and asthma admissions among children in Athens, Greece. Medical data were obtained from hospital registries of the three main Children's Hospitals in Athens during the 1978-2000 period; children were classified into two age groups: 0-4 and 5-14 years. The application of Generalized Linear Models with Poisson distribution revealed a significant relationship among asthma hospitalizations and the investigated parameters, especially for the children aged 0-4 years. Our findings showed that Hospital admissions for childhood asthma in Athens, Greece, is negatively correlated with discomfort index, air temperature and absolute humidity whereas there is a positive correlation with cooling power, relative humidity and wind speed.     

Association of meteorological factors with suicide

OBJECTIVE: To identify weather factors associated with an increased risk of suicide. In a number of prior studies an influence of meteorological conditions on the incidence of suicide or attempted suicide has been suggested. METHOD: Official data on the suicide cases of the state of Tyrol, Austria, assessed over a period of 6 years (n = 702) were correlated with a number of meteorological factors assessed at eight weather stations. RESULTS: The risk of committing suicide was significantly higher on days with high temperatures, low relative humidity or a thunderstorm and on days following a thunderstorm. The multiple logistic regression analysis left "temperature" and "thunderstorm on the preceding day" as significant factors, even after adjustment for sociodemographic and geographical variables. CONCLUSION: Within the interaction of psychological and environmental influences in the development of suicidal ideation and behaviour, specific meteorological conditions may additionally contribute to the risk of suicide in predisposed individuals.