Weather Variables Associated with Spore Dispersal of Lecanosticta acicola Causing Pine Needle Blight in Northern Spain

In the last decade, the impact of needle blight fungal pathogens on the health status of forests in northern Spain has marked a turning point in forest production systems based on Pinus radiata species. Dothistroma needle blight caused by Dothistroma septosporum and D. pini, and brown spot needle blight caused by Lecanosticta acicola, coexist in these ecosystems. There is a clear dominance of L. acicola with respect to the other two pathogens and evidence of sexual reproduction in the area. Understanding L. acicola spore dispersal dynamics within climatic determinants is necessary to establish more efficient management strategies to increase the sustainability of forest ecosystems. In this study, spore counts of 15 spore traps placed in Pinus ecosystems were recorded in 2019 and spore abundance dependency on weather data was analysed using generalised additive models. During the collection period, the model that best fit the number of trapped spores included the daily maximum temperature and daily cumulative precipitation, which was associated to higher spore counts. The presence of conidia was detected from January and maximum peaks of spore dispersal were generally observed from September to November.

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Social media reveal ecoregional variation in how weather influences visitor behavior in U.S. National Park Service units

Scientific Reports ◽

10.1038/s41598-021-82145-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Emily J. Wilkins ◽

Peter D. Howe ◽

Jordan W. Smith

Keyword(s):

National Park ◽

National Park Service ◽

Spatial Behavior ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Daily Weather ◽

Visitor Behavior ◽

Park Service ◽

The Impact

AbstractDaily weather affects total visitation to parks and protected areas, as well as visitors’ experiences. However, it is unknown if and how visitors change their spatial behavior within a park due to daily weather conditions. We investigated the impact of daily maximum temperature and precipitation on summer visitation patterns within 110 U.S. National Park Service units. We connected 489,061 geotagged Flickr photos to daily weather, as well as visitors’ elevation and distance to amenities (i.e., roads, waterbodies, parking areas, and buildings). We compared visitor behavior on cold, average, and hot days, and on days with precipitation compared to days without precipitation, across fourteen ecoregions within the continental U.S. Our results suggest daily weather impacts where visitors go within parks, and the effect of weather differs substantially by ecoregion. In most ecoregions, visitors stayed closer to infrastructure on rainy days. Temperature also affects visitors’ spatial behavior within parks, but there was not a consistent trend across ecoregions. Importantly, parks in some ecoregions contain more microclimates than others, which may allow visitors to adapt to unfavorable conditions. These findings suggest visitors’ spatial behavior in parks may change in the future due to the increasing frequency of hot summer days.

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Climate Influences on Meningitis Incidence in Northwest Nigeria

Weather Climate and Society ◽

10.1175/wcas-d-13-00004.1 ◽

2014 ◽

Vol 6 (1) ◽

pp. 62-76 ◽

Cited By ~ 9

Author(s):

Auwal F. Abdussalam ◽

Andrew J. Monaghan ◽

Vanja M. Dukić ◽

Mary H. Hayden ◽

Thomas M. Hopson ◽

...

Keyword(s):

Generalized Linear Models ◽

Linear Models ◽

Generalized Additive Models ◽

Skill Score ◽

Additive Models ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Explanatory Variables ◽

Score Statistics

Abstract Northwest Nigeria is a region with a high risk of meningitis. In this study, the influence of climate on monthly meningitis incidence was examined. Monthly counts of clinically diagnosed hospital-reported cases of meningitis were collected from three hospitals in northwest Nigeria for the 22-yr period spanning 1990–2011. Generalized additive models and generalized linear models were fitted to aggregated monthly meningitis counts. Explanatory variables included monthly time series of maximum and minimum temperature, humidity, rainfall, wind speed, sunshine, and dustiness from weather stations nearest to the hospitals, and the number of cases in the previous month. The effects of other unobserved seasonally varying climatic and nonclimatic risk factors that may be related to the disease were collectively accounted for as a flexible monthly varying smooth function of time in the generalized additive models, s(t). Results reveal that the most important explanatory climatic variables are the monthly means of daily maximum temperature, relative humidity, and sunshine with no lag; and dustiness with a 1-month lag. Accounting for s(t) in the generalized additive models explains more of the monthly variability of meningitis compared to those generalized linear models that do not account for the unobserved factors that s(t) represents. The skill score statistics of a model version with all explanatory variables lagged by 1 month suggest the potential to predict meningitis cases in northwest Nigeria up to a month in advance to aid decision makers.

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Frequency of compound hot-dry weather extremes has significantly increased in Australia since 1889

10.20944/preprints202102.0012.v1 ◽

2021 ◽

Author(s):

Brian Collins

Keyword(s):

Temporal Variations ◽

Nonparametric Tests ◽

Weather Data ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Grid Cells ◽

Weather Extremes ◽

Spatial Homogeneity ◽

Daily Weather Data

There is high confidence that climate change has increased the probability of concurrent temperature-precipitation extremes, changed their spatial-temporal variations, and affected the relationships between drivers of such natural hazards. However, the extent of such changes has been less investigated in Australia. Daily weather data (131 years, 1889-2019) at 700 grid cells (1◦ × 1◦) across Australia was obtained to calculate annual and seasonal mean daily maximum temperature (MMT) and total precipitation (TPR). A nonparametric multivariate copula framework was adopted to estimate the return period of compound hot-dry (CHD) events based on an ‘And’ hazard scenario (hotter than a threshold ‘And’ drier than a threshold). CHD extremes were defined as years with joint return periods of larger than 25 years. Mann-Kendall nonparametric tests was used to analyse trends in MMT and TPR as well as in the frequency of univariate and CHD extremes. A general cooling-wetting trend was observed over 1889-1989. Significant increasing trends were detected over 1990-2019 in the frequency and severity of hot extremes across the country while trends in dry extremes were mostly insignificant (and decreasing). Results showed a significant increase in the association between temperature and precipitation at various temporal scales. The frequency of CHD extremes was mostly stable over 1889-1989, but significantly increased between 1990 and 2019 at 44% of studied grid cells, mostly located in the north, south-east and south-west. Spatial homogeneity (i.e. connectedness) and propagation of extreme events from one grid cell to its neighbouring cells was investigated across Australia. It can be concluded that this connectedness has not significantly changed since 1889.

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Mycosphaerella dearnessii, a Needle-cast Pathogen on Mountain Pine (Pinus mugo) in Italy

Plant Disease ◽

10.1094/pdis.2000.84.8.922a ◽

2000 ◽

Vol 84 (8) ◽

pp. 922-922 ◽

Cited By ~ 9

Author(s):

N. La Porta ◽

P. Capretti

Keyword(s):

Botanical Garden ◽

Climatic Conditions ◽

Brown Spot ◽

Pinus Mugo ◽

Eastern United States ◽

Lake Garda ◽

Northeastern Italy ◽

The Alps ◽

Lecanosticta Acicola ◽

Needle Blight

The pathogen Mycosphaerella dearnessii Barr. (syn. Scirrhia acicola; anamorph Lecanosticta acicola), the causal agent of brown spot needle blight, was observed on Pinus mugo in the Botanical Garden in Gardone (Brescia), on the western side of Garda Lake in northeastern Italy. Symptoms were first noticed in the spring of 1997 by Klaus Lang (University of Freising, Germany). Two years later, all 12 of the P. mugo present in the Garden exhibited extensive necrosis, and defoliation of the crown starting from the bottom upward was more prevalent on the shaded portion of infected trees. The trees were about 50 years old and 2.0 to 2.5 m in height. Symptomatic needles were confined to the 2- and 3-year old internodes. Infected needles had several dark to purplish-brown spots surrounded by green tissue and usually had dead tips. Pycnidia and conidia of Lecanosticta acicola were observed. Conidia were 4-celled, curved, pointed at one end and blunt at the other, pale olive-brown and 20 to 30 × 3 to 4 μm. The fungus was isolated in pure culture. The pathogen causes serious losses in China, eastern United States, and central and South America, but was observed for the first time in Europe only 30 years ago. It is a major cause of needle blight on several European pine species, especially P. sylvestris, P. nigra, and P. mugo. In the last 7 years, there have been reports of the fungus in pine stands, first in France, Aquitaine, and the western Pyrenees on P. radiata (3), and more recently on P. mugo in the Alps in Austria (1), Switzerland (2), and southern Germany (4). This record of the fungus near Lake Garda poses a new serious threat especially for the pine plantations of P. nigra and P. sylvestris in the more humid locations in the Alps, Apennines, and elsewhere in the mountains of southern Europe where the climatic conditions are similar to that of central Europe. This is the first report of M. dearnessii on the southern slopes of the Alps and in Italy. References: (1) M. Brandstetter and T. Cech. Oesterreichische Forstzeitung 110:35, 1999. (2) O. Holdenrieder and T. N. Sieber. Eur. J. For. Pathol. 25:293, 1995. (3) A. Levy and C. Lafaurie. Phytoma 463:33, 1994. (4) L. Pehl L. Nachrichtenbl. Dtsch. Pflanzenschutzdienstes 47:305, 1995.

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Impact of mass human migration during Chinese New Year on Beijing urban heat island

10.5194/egusphere-egu2020-6250 ◽

2020 ◽

Author(s):

Jingjing Dou ◽

Shiguang Miao

Keyword(s):

Spatial Distribution ◽

Urban Heat Island ◽

Human Migration ◽

Floating Population ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Heat Island ◽

Urban Heat ◽

The Impact

<p>The Chinese New Year (CNY, also called Spring Festival), which officially lasts for 7 days, is the most important holiday in China. Chinese people in large cities usually return to their hometowns for family reunions before the CNY holiday and return afterward. Nearly half of Beijing&#8217;s population has been reported to leave the city for family reunions before the CNY holidays in the past several years. Hourly automatic weather station (AWS) data during CNY 2010-2015 were used to analyze the changes in the temporal and spatial distribution of the Beijing urban heat island intensity (UHII) and the impact of mass human migration on urban temperature. Soil moisture, 10-m wind speed, and cloud cover were considered and indicated nearly no change during the pre-CNY period (2 to 4 weeks before CNY) and CNY week, which means that UHII variation was mainly affected by the mass human migration. Daily UHII during CNY week was lower than during pre-CNY period. UHII for daily maximum temperature decreased by 55% during CNY week than the pre-CNY period (0.6 &#176;C during pre-CNY period vs. 0.27 &#176;C during CNY week) due to mass human migration, which was much larger than the reduction in UHII for the daily maximum temperature (5%, 4.34 &#176;C during the pre-CNY period vs. 4.11 &#176;C during the CNY week). The spatial distribution of the UHII difference between CNY week and the pre-CNY period is closely related to the locations of functional population zones. UHII for daily maximum temperature decreases most (80%, 0.40 &#176;C during the pre-CNY period vs. 0.08 &#176;C during the CNY period) between the Third and Fourth Ring Roads (RRs), an area which experiences high human activity and has the highest floating population percentage. This study can provide suggestions for optimizing the layout of urban space and land-use structures.</p>

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How Much Do Different Land Models Matter for Climate Simulation? Part I: Climatology and Variability

Journal of Climate ◽

10.1175/2010jcli3177.1 ◽

2010 ◽

Vol 23 (11) ◽

pp. 3120-3134 ◽

Cited By ~ 31

Author(s):

Jiangfeng Wei ◽

Paul A. Dirmeyer ◽

Zhichang Guo ◽

Li Zhang ◽

Vasubandhu Misra

Keyword(s):

Heat Flux ◽

Surface Temperature ◽

Latent Heat ◽

General Circulation ◽

Surface Fluxes ◽

Heat Fluxes ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Time Step ◽

The Impact

Abstract An atmospheric general circulation model (AGCM) is coupled to three different land surface schemes (LSSs), both individually and in combination (i.e., the LSSs receive the same AGCM forcing each time step and the averaged upward surface fluxes are passed back to the AGCM), to study the uncertainty of simulated climatologies and variabilities caused by different LSSs. This tiling of the LSSs is done to study the uncertainty of simulated mean climate and climate variability caused by variations between LSSs. The three LSSs produce significantly different surface fluxes over most of the land, no matter whether they are coupled individually or in combination. Although the three LSSs receive the same atmospheric forcing in the combined experiment, the inter-LSS spread of latent heat flux can be larger or smaller than the individually coupled experiment, depending mostly on the evaporation regime of the schemes in different regions. Differences in precipitation are the main reason for the different latent heat fluxes over semiarid regions, but for sensible heat flux, the atmospheric differences and LSS differences have comparable contributions. The influence of LSS uncertainties on the simulation of surface temperature is strongest in dry seasons, and its influence on daily maximum temperature is stronger than on minimum temperature. Land–atmosphere interaction can dampen the impact of LSS uncertainties on surface temperature in the tropics, but can strengthen their impact in middle to high latitudes. Variations in the persistence of surface heat fluxes exist among the LSSs, which, however, have little impact on the global pattern of precipitation persistence. The results provide guidance to future diagnosis of model uncertainties related to LSSs.

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Impact Assessment of Climate Change on Paddy Yield: A Case Study of Nepal Agriculture Research Council (NARC), Tarahara, Nepal

Journal of the Institute of Engineering ◽

10.3126/jie.v8i3.5941 ◽

1970 ◽

Vol 8 (3) ◽

pp. 147-167 ◽

Cited By ~ 3

Author(s):

Yam K Rai ◽

Bhakta B Ale ◽

Jawed Alam

Keyword(s):

Climate Change ◽

Solar Radiation ◽

Research Council ◽

Crop Yields ◽

Rice Yield ◽

Weather Data ◽

Maximum Temperature ◽

Future Climate Change ◽

Climate Change Scenarios ◽

The Impact

Climate change and global warming are burning issues, which significantly threat agriculture and global food security. Change in solar radiation, temperature and precipitation will influence the change in crop yields and hence economy of agriculture. It is possible to understand the phenomenon of climate change on crop production and to develop adaptation strategies for sustainability in food production, using a suitable crop simulation model. CERES-Rice model of DSSAT v4.0 was used to simulate the rice yield of the region under climate change scenarios using the historical weather data at Nepal Agriculture Research Council (NARC) Tarahara (1989-2008). The Crop Model was calibrated using the experimental crop data, climate data and soil data for two years (2000-2001) and was validated by using the data of the year 2002 at NARC Tarahara. In this study various scenarios were undertaken to analyze the rice yield. The change in values of weather parameters due to climate change and its effects on the rice yield were studied. It was observed that increase in maximum temperature up to 2°C and 1°C in minimum temperature have positive impact on rice yield but beyond that temperature it was observed negative impact in both cases of paddy production in ambient temperature. Similarly, it was observed that increased in mean temperature, have negative impacts on rice yield. The impact of solar radiation in rice yield was observed positive during the time of study period. Adjustments were made in the fertilizer rate, plant density per square meter, planting date and application of water rate to investigate suitable agronomic options for adaptation under the future climate change scenarios. Highest yield was obtained when the water application was increased up to 3 mm depth and nitrogen application rate was 140 kg/ha respectively. DOI: http://dx.doi.org/10.3126/jie.v8i3.5941 JIE 2011; 8(3): 147-167

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The impact of differences in large-scale circulation output from climate models on the regional modeling of ozone and PM

Atmospheric Chemistry and Physics ◽

10.5194/acp-12-9441-2012 ◽

2012 ◽

Vol 12 (20) ◽

pp. 9441-9458 ◽

Cited By ~ 41

Author(s):

A. M. M. Manders ◽

E. van Meijgaard ◽

A. C. Mues ◽

R. Kranenburg ◽

L. H. van Ulft ◽

...

Keyword(s):

Air Quality ◽

Climate Models ◽

Climate Model ◽

Transport Model ◽

Global Climate Model ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Climate Simulations ◽

The Impact

Abstract. Climate change may have an impact on air quality (ozone, particulate matter) due to the strong dependency of air quality on meteorology. The effect is often studied using a global climate model (GCM) to produce meteorological fields that are subsequently used by chemical transport models. However, climate models themselves are subject to large uncertainties and fail to reproduce the present-day climate adequately. The present study illustrates the impact of these uncertainties on air quality. To this end, output from the SRES-A1B constraint transient runs with two GCMs, i.e. ECHAM5 and MIROC-hires, has been dynamically downscaled with the regional climate model RACMO2 and used to force a constant emission run with the chemistry transport model LOTOS-EUROS in a one-way coupled run covering the period 1970–2060. Results from the two climate simulations have been compared with a RACMO2-LOTOS-EUROS (RLE) simulation forced by the ERA-Interim reanalysis for the period 1989–2009. Both RLE_ECHAM and RLE_MIROC showed considerable deviations from RLE_ERA for daily maximum temperature, precipitation and wind speed. Moreover, sign and magnitude of these deviations depended on the region. The differences in average present-day concentrations between the simulations were equal to (RLE_MIROC) or even larger than (RLE_ECHAM) the differences in concentrations between present-day and future climate (2041–2060). The climate simulations agreed on a future increase in average summer ozone daily maximum concentrations of 5–10 μg m−3 in parts of Southern Europe and a smaller increase in Western and Central Europe. Annual average PM10 concentrations increased 0.5–1.0 μg m−3 in North-West Europe and the Po Valley, but these numbers are rather uncertain: overall, changes for PM10 were small, both positive and negative changes were found, and for many locations the two climate runs did not agree on the sign of the change. This illustrates that results from individual climate runs can at best indicate tendencies and should therefore be interpreted with great care.

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Simulating the Impact of Climate Change on Growth and Yield of Maize Using CERES-Maize Model under Temperate Kashmir

Current Journal of Applied Science and Technology ◽

10.9734/cjast/2019/v35i130166 ◽

2019 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Bilal Ahmad Lone ◽

Shivam Tripathi ◽

Asma Fayaz ◽

Purshotam Singh ◽

Sameera Qayoom ◽

...

Keyword(s):

Climate Variability ◽

Minimum Temperature ◽

Maize Yield ◽

Climate Impact ◽

Growth And Yield ◽

Weather Data ◽

Maximum Temperature ◽

The Impact ◽

Global Food ◽

Process Based Models

Climate variability has been and continues to be, the principal source of fluctuations in global food production in countries of the developing world and is of serious concern. Process-based models use simplified functions to express the interactions between crop growth and the major environmental factors that affect crops (i.e., climate, soils and management), and many have been used in climate impact assessments. Average of 10 years weather data from 1985 to 2010, maximum temperature shows an increasing trend ranges from 18.5 to 20.5°C.This means there is an increase of 2°C within a span of 25 years. Decreasing trend was observed with respect to precipitation was observed with the same data. The magnitude of decrease was from 925 mm to 650 mm of rainfall which is almost decrease of 275 mm of rainfall in 25 years. Future climate for 2011-2090 from A1B scenario extracted from PRECIS run shows that overall maximum and minimum temperature increase by 5.39°C (±1.76) and 5.08°C (±1.37) also precipitation will decrease by 3094.72 mm to 2578.53 (±422.12) The objective of this study was to investigate the effects of climate variability and change on maize growth and yield of Srinagar Kashmir. Two enhanced levels of temperature (maximum and minimum by 2 and 4°C) and CO2 enhanced by 100 ppm & 200 ppm were used in this study with total combinations of 9 with one normal condition. Elevation of maximum and minimum temperature by 4°C anthesis and maturity of maize was earlier 14 days with a deviation of 18% and 26 days with a deviation of 20% respectively. Increase in temperature by 2 to 4°C alone or in combination with enhanced levels of CO2 by 100 and 200 ppm the growth and yield of maize was drastically declined with an reduction of about 40% in grain yield. Alone enhancement of CO2 at both the levels fails show any significant impact on maize yield.

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Estimating the Impact of Global Warming on Aircraft Takeoff Performance in China

Atmosphere ◽

10.3390/atmos12111472 ◽

2021 ◽

Vol 12 (11) ◽

pp. 1472

Author(s):

Wei Yuan ◽

Panxi Dai ◽

Mengxiang Xu ◽

Wei Song ◽

Peng Zhang

Keyword(s):

Global Warming ◽

Critical Temperature ◽

Carrying Capacity ◽

East China ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Climate Projections ◽

West China ◽

The Impact

Aviation operations are significantly affected by weather conditions, such as high-temperature days. Under global warming, rising temperatures decrease the air density and thus, reduce the maximum takeoff weight of an aircraft. In this study, we investigate the impact of global warming on the aircraft takeoff performance in 53 airports in China by combining observational data and CMIP6 climate projections. There is a distinct geographic inhomogeneity of critical temperature, above which the takeoff weight decreases significantly with the increasing air temperature, mostly due to differences in airport elevations. By the end of the century, under the SSP5-8.5 scenario (with average warming of 5.2 °C in China), the daily maximum temperature for nearly all summer days in West China and for about half of the summer days in East China exceeds critical temperature, indicating that frequent weight restriction will be necessary. We further examine the reduction in carrying capacity due to climate change. By the end of the century, under the SSP5-8.5 scenario, the summer total carrying capacity will be reduced by about 2.8% averaged over all 53 airports. The impacts on airports in West China are nearly four times greater than those in East China, due to the higher vulnerability and stronger warming in West China.

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