scholarly journals A Comparison of Bangladesh Climate Surfaces from the Geostatistical Point of View

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Avit Kumar Bhowmik
Keyword(s):  
Spatial Interpolation ◽  
Point Of View ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Climate Indices ◽  
Daily Maximum ◽  
Prediction Ability ◽  
Distance Weighting ◽  
Annual Total ◽  
Yearly Maximum

This study analyses the degree and margin of differences among the surfaces of annual total precipitation in wet days (PRCPTOT) and the yearly maximum value of the daily maximum temperature (TXx) of Bangladesh, produced by thin plate spline (TPS), inverse distance weighting (IDW), ordinary kriging (OK), and universal kriging (UK) methods of spatial interpolation. From the surface differences, the maximum and minimum differences are observed between the surfaces produced by TPS and IDW, and OK and UK, respectively. The residual plots from cross-validation depict that IDW and OK methods mostly under predict and TPS and UK methods mostly overpredict the observed climate indices’ values. Both the tendency of methods’ over and underprediction and the surface-differences decrease with the increase in the number of available spatial point observations. Finally, two performance measures—the index of agreement (d) and the coefficient of variation of prediction (ρf)—imply that there is a little difference in the prediction ability of the four different methods. The performance of the spatial interpolation improves with the increase in the number of available spatial points, and eventually the predicted climate surfaces get similar. However, UK shows better interpolation performance in most of the years.

scholarly journals Trends in Extreme Climate Indices in Cherrapunji for the period 1979 to 2020

2021 ◽  
Author(s):  
Raju Kalita ◽  
Dipangkar Kalita ◽  
Atul Saxena
Keyword(s):  
Climate Change ◽  
Negative Trend ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Trend Test ◽  
Climate Indices ◽  
Daily Maximum ◽  
Positive Trend ◽  
Extreme Climate

Abstract We have used Mann-Kendall trend test and Sen’s slope estimator method to find out significant changes in extreme climate indices for daily temperature as well as precipitation over the period 1979 to 2020 in Cherrapunji. In the present study, a total of 24 precipitation and temperature based extreme climate indices were calculated using RClimDex v 1.9-3. Among 24 indices, 7 were derived from number of days above nn mm rainfall (Rnn) according to Indian Meteorological Department (IMD) convention and the rest were in accordance with the Expert Team on Climate Change Detection and Indices (ETCCDI). It was observed that, among all the indices, consecutive dry days (CDD), summer days (SU25) and very light rainfall (VLR) days increased significantly with 0.54, 1.58 and 0.14 days/year respectively, while only consecutive wet days (CWD) decreased significantly with 0.36 days/year. A slight negative trend was also observed in case of tropical nights (TR20) and among the other precipitation indices as well. Again, the indices associated with daily maximum temperature increased significantly with annual change of 0.06 to 0.07 ⁰C/year. And for indices associated with daily minimum temperature, almost no change or a slight negative change was observed, except a significant positive trend in February and significant negative trend in November for TNN only. The analysis reveals that some of the extreme climate indices which explains the climatic conditions of Cherrapunji has changed a lot over the period of 42 years and if this trend continues then Cherrapunji will be under threat when it comes to climate change.

Simulation-based indices for a climate-resilient agriculture - insights from ADAPTER

2021 ◽  
Author(s):  
Sebastian Bathiany ◽  
Diana Rechid ◽  
Klaus Goergen ◽  
Patrizia Ney ◽  
Alexandre Belleflamme
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Climate Indices ◽  
Agricultural System ◽  
Daily Maximum ◽  
Public Attention ◽  
Genetic Traits ◽  
Changing Climate

<p>Agriculture is among the sectors that are most vulnerable to extreme weather conditions and climate change. In Germany, the dry and hot summers 2018, 2019, and 2020 have brought this into the focus of public attention. Agricultural actors like farmers, advisors or companies are concerned to adapt to interannual climate variability and extremes. In the ADAPTER project, we collaborate with stakeholders from these groups and generate practically relevant information, tailored climate change indices and usable information products.</p> <p> </p> <p>The challenges of climate change for agriculture are manifold. The genetic traits of crops need to be adapted to a new climatic average, for instance by breeding new sorts of crops that are specialised for warmer and dryer conditions (i.e. maximising average yields). Agricultural practises need to be adapted to changing seasonal weather patterns under changing climate conditions. It is also vital to ensure the resilience to climate extremes by aiming for a low inter-annual yield variability, in order to prevent price shocks or food shortages.</p> <p> </p> <p>In order to adequately determine the optimal balance between specialisation and risk diversification, the agricultural sector hence requires knowledge not only about changes in the mean climate, but also on the variance around the changing mean. In this contribution, we focus on this second aspect by analysing the potential impact of forced changes in climate variability on the stability of crop yields in central Europe.</p> <p> </p> <p>We analyse the changing climate variability in 85 regional climate model projections from Coordinated Downscaling Experiments over Europe (EURO-CORDEX). We first show how the projections indicate a general increase in climate variability during critical development stages of wheat, rapeseed and maize in Europe. Second, we determine several more specific agronomic climate indices that capture events that have previously been shown to be critical for yields, for instance the occurrence of high daily maximum temperature, the seasonal sum of rainfall, the number of dry days, or the occurrence of compound events with simultaneous drought and increased temperatures. Finally, we illustrate how the results can be made accessible to practitioners in the agriculture sector by co-designing interactive browser applications, thus directly supporting the adaptation of the agricultural system to climate change.</p> <p> </p>

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

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.

scholarly journals Joint Occurrence of Daily Temperature and Precipitation Extreme Events over Canada

2014 ◽  
Vol 53 (9) ◽  
pp. 2148-2162 ◽  
Author(s):  
Bárbara Tencer ◽  
Andrew Weaver ◽  
Francis Zwiers
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Heavy Precipitation ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Negative Consequences ◽  
Temperature And Precipitation ◽  
Joint Occurrence

AbstractThe occurrence of individual extremes such as temperature and precipitation extremes can have a great impact on the environment. Agriculture, energy demands, and human health, among other activities, can be affected by extremely high or low temperatures and by extremely dry or wet conditions. The simultaneous or proximate occurrence of both types of extremes could lead to even more profound consequences, however. For example, a dry period can have more negative consequences on agriculture if it is concomitant with or followed by a period of extremely high temperatures. This study analyzes the joint occurrence of very wet conditions and high/low temperature events at stations in Canada. More than one-half of the stations showed a significant positive relationship at the daily time scale between warm nights (daily minimum temperature greater than the 90th percentile) or warm days (daily maximum temperature above the 90th percentile) and heavy-precipitation events (daily precipitation exceeding the 75th percentile), with the greater frequencies found for the east and southwest coasts during autumn and winter. Cold days (daily maximum temperature below the 10th percentile) occur together with intense precipitation more frequently during spring and summer. Simulations by regional climate models show good agreement with observations in the seasonal and spatial variability of the joint distribution, especially when an ensemble of simulations was used.

scholarly journals Observed Trends in Indices of Daily Temperature Extremes in South America 1960–2000

2005 ◽  
Vol 18 (23) ◽  
pp. 5011-5023 ◽  
Author(s):  
L. A. Vincent ◽  
T. C. Peterson ◽  
V. R. Barros ◽  
M. B. Marino ◽  
M. Rusticucci ◽  
...  
Keyword(s):  
Climate Change ◽  
South America ◽  
Climate Extremes ◽  
Daily Temperature ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Increasing Trends

Abstract A workshop on enhancing climate change indices in South America was held in Maceió, Brazil, in August 2004. Scientists from eight southern countries brought daily climatological data from their region for a meticulous assessment of data quality and homogeneity, and for the preparation of climate change indices that can be used for analyses of changes in climate extremes. This study presents an examination of the trends over 1960–2000 in the indices of daily temperature extremes. The results indicate no consistent changes in the indices based on daily maximum temperature while significant trends were found in the indices based on daily minimum temperature. Significant increasing trends in the percentage of warm nights and decreasing trends in the percentage of cold nights were observed at many stations. It seems that this warming is mostly due to more warm nights and fewer cold nights during the summer (December–February) and fall (March–May). The stations with significant trends appear to be located closer to the west and east coasts of South America.

Assessing the contribution of multiple forcings to changes in temperature extremes 1981–2020 using CMIP6 climate models

2021 ◽  
Author(s):  
Mastawesha Misganaw Engdaw ◽  
Andrew Ballinger ◽  
Gabriele Hegerl ◽  
Andrea Steiner
Keyword(s):  
Climate Change ◽  
Greenhouse Gases ◽  
Climate Models ◽  
Temporal Trends ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Cold Temperatures

<p>In this study, we aim at quantifying the contribution of different forcings to changes in temperature extremes over 1981–2020 using CMIP6 climate model simulations. We first assess the changes in extreme hot and cold temperatures defined as days below 10% and above 90% of daily minimum temperature (TN10 and TN90) and daily maximum temperature (TX10 and TX90). We compute the change in percentage of extreme days per season for October-March (ONDJFM) and April-September (AMJJAS). Spatial and temporal trends are quantified using multi-model mean of all-forcings simulations. The same indices will be computed from aerosols-, greenhouse gases- and natural-only forcing simulations. The trends estimated from all-forcings simulations are then attributed to different forcings (aerosols-, greenhouse gases-, and natural-only) by considering uncertainties not only in amplitude but also in response patterns of climate models. The new statistical approach to climate change detection and attribution method by Ribes et al. (2017) is used to quantify the contribution of human-induced climate change. Preliminary results of the attribution analysis show that anthropogenic climate change has the largest contribution to the changes in temperature extremes in different regions of the world.</p><p><strong>Keywords:</strong> climate change, temperature, extreme events, attribution, CMIP6</p><p> </p><p><strong>Acknowledgement:</strong> This work was funded by the Austrian Science Fund (FWF) under Research Grant W1256 (Doctoral Programme Climate Change: Uncertainties, Thresholds and Coping Strategies)</p>

scholarly journals The effects of ambient temperature and heatwaves on dailyCampylobactercases in Adelaide, Australia, 1990–2012

2017 ◽  
Vol 145 (12) ◽  
pp. 2603-2610 ◽  
Author(s):  
A. MILAZZO ◽  
L. C. GILES ◽  
Y. ZHANG ◽  
A. P. KOEHLER ◽  
J. E. HILLER ◽  
...  
Keyword(s):  
Regression Models ◽  
South Australia ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Infectious Gastroenteritis ◽  
Food Borne ◽  
Relationship Of ◽  
The Relationship

SUMMARYCampylobacterspp. is a commonly reported food-borne disease with major consequences for morbidity. In conjunction with predicted increases in temperature, proliferation in the survival of microorganisms in hotter environments is expected. This is likely to lead, in turn, to an increase in contamination of food and water and a rise in numbers of cases of infectious gastroenteritis. This study assessed the relationship ofCampylobacterspp. with temperature and heatwaves, in Adelaide, South Australia.We estimated the effect of (i) maximum temperature and (ii) heatwaves on dailyCampylobactercases during the warm seasons (1 October to 31 March) from 1990 to 2012 using Poisson regression models.There was no evidence of a substantive effect of maximum temperature per 1 °C rise (incidence rate ratio (IRR) 0·995, 95% confidence interval (95% CI) 0·993–0·997) nor heatwaves (IRR 0·906, 95% CI 0·800–1·026) onCampylobactercases. In relation to heatwave intensity, which is the daily maximum temperature during a heatwave, notifications decreased by 19% within a temperature range of 39–40·9 °C (IRR 0·811, 95% CI 0·692–0·952). We found little evidence of an increase in risk and lack of association betweenCampylobactercases and temperature or heatwaves in the warm seasons. Heatwave intensity may play a role in that notifications decreased with higher temperatures. Further examination of the role of behavioural and environmental factors in an effort to reduce the risk of increasedCampylobactercases is warranted.

scholarly journals Relations between Temperature and Residential Natural Gas Consumption in the Central and Eastern United States

2007 ◽  
Vol 46 (11) ◽  
pp. 1993-2013 ◽  
Author(s):  
Reed P. Timmer ◽  
Peter J. Lamb
Keyword(s):  
United States ◽  
Natural Gas ◽  
Time Scales ◽  
The United States ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
The South ◽  
Northern Regions ◽  
Gas Consumption

Abstract The increased U.S. natural gas price volatility since the mid-to-late-1980s deregulation generally is attributed to the deregulated market being more sensitive to temperature-related residential demand. This study therefore quantifies relations between winter (November–February; December–February) temperature and residential gas consumption for the United States east of the Rocky Mountains for 1989–2000, by region and on monthly and seasonal time scales. State-level monthly gas consumption data are aggregated for nine multistate subregions of three Petroleum Administration for Defense Districts of the U.S. Department of Energy. Two temperature indices [days below percentile (DBP) and heating degree-days (HDD)] are developed using the Richman–Lamb fine-resolution (∼1° latitude–longitude) set of daily maximum and minimum temperatures for 1949–2000. Temperature parameters/values that maximize DBP/HDD correlations with gas consumption are identified. Maximum DBP and HDD correlations with gas consumption consistently are largest in the Great Lakes–Ohio Valley region on both monthly (from +0.89 to +0.91) and seasonal (from +0.93 to +0.97) time scales, for which they are based on daily maximum temperature. Such correlations are markedly lower on both time scales (from +0.62 to +0.80) in New England, where gas is less important than heating oil, and on the monthly scale (from +0.55 to +0.75) across the South because of low January correlations. For the South, maximum correlations are for daily DBP and HDD indices based on mean or minimum temperature. The percentiles having the highest DBP index correlations with gas consumption are slightly higher for northern regions than across the South. This is because lower (higher) relative (absolute) temperature thresholds are reached in warmer regions before home heating occurs. However, these optimum percentiles for all regions are bordered broadly by surrounding percentiles for which the correlations are almost as high as the maximum. This consistency establishes the robustness of the temperature–gas consumption relations obtained. The reference temperatures giving the highest HDD correlations with gas consumption are lower for the colder northern regions than farther south where the temperature range is truncated. However, all HDD reference temperatures greater than +10°C (+15°C) yield similar such correlations for northern (southern) regions, further confirming the robustness of the findings. This robustness, coupled with the very high correlation magnitudes obtained, suggests that potentially strong gas consumption predictability would follow from accurate seasonal temperature forecasts.

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