scholarly journals Evaluation of the AR4 Climate Models’ Simulated Daily Maximum Temperature, Minimum Temperature, and Precipitation over Australia Using Probability Density Functions

2007 ◽  
Vol 20 (17) ◽  
pp. 4356-4376 ◽  
Author(s):  
S. E. Perkins ◽  
A. J. Pitman ◽  
N. J. Holbrook ◽  
J. McAneney
Keyword(s):  
Probability Density ◽  
Minimum Temperature ◽  
Climate Models ◽  
Quantitative Measure ◽  
Probability Density Functions ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Density Functions ◽  
Coupled Climate Models

Abstract The coupled climate models used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change are evaluated. The evaluation is focused on 12 regions of Australia for the daily simulation of precipitation, minimum temperature, and maximum temperature. The evaluation is based on probability density functions and a simple quantitative measure of how well each climate model can capture the observed probability density functions for each variable and each region is introduced. Across all three variables, the coupled climate models perform better than expected. Precipitation is simulated reasonably by most and very well by a small number of models, although the problem with excessive drizzle is apparent in most models. Averaged over Australia, 3 of the 14 climate models capture more than 80% of the observed probability density functions for precipitation. Minimum temperature is simulated well, with 10 of the 13 climate models capturing more than 80% of the observed probability density functions. Maximum temperature is also reasonably simulated with 6 of 10 climate models capturing more than 80% of the observed probability density functions. An overall ranking of the climate models, for each of precipitation, maximum, and minimum temperatures, and averaged over these three variables, is presented. Those climate models that are skillful over Australia are identified, providing guidance on those climate models that should be used in impacts assessments where those impacts are based on precipitation or temperature. These results have no bearing on how well these models work elsewhere, but the methodology is potentially useful in assessing which of the many climate models should be used by impacts groups.

scholarly journals Assessment of Reanalysis Daily Extreme Temperatures with China’s Homogenized Historical Dataset during 1979–2001 Using Probability Density Functions

2010 ◽  
Vol 23 (24) ◽  
pp. 6605-6623 ◽  
Author(s):  
Jiafu Mao ◽  
Xiaoying Shi ◽  
Lijuan Ma ◽  
Dale P. Kaiser ◽  
Qingxiang Li ◽  
...  
Keyword(s):  
Probability Density ◽  
Skill Score ◽  
Probability Density Functions ◽  
Department Of Energy ◽  
Maximum Temperature ◽  
Daily Maximum ◽  
Cold Bias ◽  
Density Functions ◽  
Extreme Temperatures ◽  
Skill Scores

Abstract Using a recently homogenized observational daily maximum (TMAX) and minimum temperature (TMIN) dataset for China, the extreme temperatures from the 40-yr ECMWF Re-Analysis (ERA-40), the Japanese 25-year Reanalysis (JRA-25), the NCEP/Department of Energy Global Reanalysis 2 (NCEP-2), and the ECMWF’s ERA-Interim (ERAIn) reanalyses for summer (June–August) and winter (December–February) are assessed by probability density functions for the periods 1979–2001 and 1990–2001. For 1979–2001, no single reanalysis appears to be consistently accurate across eight areas examined over China. The ERA-40 and JRA-25 reanalyses show similar representations and close skill scores over most of the regions of China for both seasons. NCEP-2 generally has lower skill scores, especially over regions with complex topography. The regional and seasonal differences identified are commonly associated with different geographical locations and the methods used to diagnose these quantities. All the selected reanalysis products exhibit better performance for winter compared to summer over most regions of China. The TMAX values from the reanalysis tend to be systematically underestimated, while TMIN is systematically closer to observed values than TMAX. Comparisons of the reanalyses to reproduce the 99.7 percentiles for TMAX and 0.3 percentiles for TMIN show that most reanalyses tend to underestimate the 99.7 percentiles in maximum temperature both in summer and winter. For the 0.3 percentiles in TMIN, NCEP-2 is relatively inaccurate with a −12°C cold bias over the Qinghai–Tibetan Plateau in winter. ERA-40 and JRA-25 generally overestimate the extreme TMIN, and the extreme percentage differences of ERA-40 and JRA-25 are quite similar over all of the regions. The results are generally similar for 1990–2001, but in contrast to the other three reanalysis products the newly released ERAIn is very reasonable, especially for wintertime TMIN, with a skill score greater than 0.83 for each region of China. This demonstrates the great potential of this product for use in future impact assessments on continental scales where those impacts are based on extreme temperatures.

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.

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 Use of Weather Variables to Quantify Sorghum Ergot Potential in South Africa

Plant Disease ◽  
1998 ◽  
Vol 82 (1) ◽  
pp. 26-29 ◽  
Author(s):  
N. W. McLaren ◽  
B. C. Flett
Keyword(s):  
Minimum Temperature ◽  
Pollen Viability ◽  
Breakdown Point ◽  
Field Trials ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Weather Variables ◽  
Data Set ◽  
Study Results

Quantification of resistance to ergot requires that the observed ergot severity within a sorghum line be compared with expected ergot severity (ergot potential) to compensate for differences in environmental favorability for the disease among flowering dates and seasons. The ergot potential required to induce the onset of disease is referred to as the ergot breakdown point of that line. In earlier studies, the ergot potential of a specific flowering date was defined as the mean ergot severity in all sorghum heads over all lines in the nursery which commenced flowering on that date in a genetically broad-based sorghum nursery. In this study, results of field trials enabled accurate prediction of ergot potential by using a multiple regression analysis which included three weather variables—namely, pre-flowering minimum temperature (mean of days 23 to 27 pre-flowering), mean daily maximum temperature, and mean daily maximum relative humidity (mean of days 1 to 5 post-flowering; R2 = 0.90; P = 0.91E-5). Evaluation of predicted and observed ergot severity in an independent data set gave an index of agreement of d = 0.94 and R2 = 0.84 (P = 0.106E-4), showing that ergot severity, assuming the presence of viable inoculum, can be accurately predicted. Low pre-flowering minimum temperature was associated with reduced pollen viability, which appeared to be the primary factor predisposing lines to ergot.

scholarly journals A Stepwise-Clustered Simulation Approach for Projecting Future Heat Wave Over Guangdong Province

2021 ◽  
Vol 9 ◽  
Author(s):  
Jiayan Ren ◽  
Guohe Huang ◽  
Yongping Li ◽  
Xiong Zhou ◽  
Jinliang Xu ◽  
...  
Keyword(s):  
Heat Wave ◽  
Climate Models ◽  
Extreme Event ◽  
Heat Waves ◽  
Extreme Temperature ◽  
Global Climate Models ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Simulation Approach

A heat wave is an important meteorological extreme event related to global warming, but little is known about the characteristics of future heat waves in Guangdong. Therefore, a stepwise-clustered simulation approach driven by multiple global climate models (i.e., GCMs) is developed for projecting future heat waves over Guangdong under two representative concentration pathways (RCPs). The temporal-spatial variations of four indicators (i.e., intensity, total intensity, frequency, and the longest duration) of projected heat waves, as well as the potential changes in daily maximum temperature (i.e., Tmax) for future (i.e., 2006–2095) and historical (i.e., 1976–2005) periods, were analyzed over Guangdong. The results indicated that Guangdong would endure a notable increasing annual trend in the projected Tmax (i.e., 0.016–0.03°C per year under RCP4.5 and 0.027–0.057°C per year under RCP8.5). Evaluations of the multiple GCMs and their ensemble suggested that the developed approach performed well, and the model ensemble was superior to any single GCM in capturing the features of heat waves. The spatial patterns and interannual trends displayed that Guangdong would undergo serious heat waves in the future. The variations of intensity, total intensity, frequency, and the longest duration of heat wave are likely to exceed 5.4°C per event, 24°C, 25 days, and 4 days in the 2080s under RCP8.5, respectively. Higher variation of those would concentrate in eastern and southwestern Guangdong. It also presented that severe heat waves with stronger intensity, higher frequency, and longer duration would have significant increasing tendencies over all Guangdong, which are expected to increase at a rate of 0.14, 0.83, and 0.21% per year under RCP8.5, respectively. Over 60% of Guangdong would suffer the moderate variation of heat waves to the end of this century under RCP8.5. The findings can provide decision makers with useful information to help mitigate the potential impacts of heat waves on pivotal regions as well as ecosystems that are sensitive to extreme temperature.

scholarly journals Changes of Extreme Temperature and Its Influencing Factors in Shiyang River Basin, Northwest China

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1171
Author(s):  
Junju Zhou ◽  
Jumei Huang ◽  
Xi Zhao ◽  
Li Lei ◽  
Wei Shi ◽  
...  
Keyword(s):  
Global Warming ◽  
River Basin ◽  
Minimum Temperature ◽  
Extreme Temperature ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Extreme Temperatures ◽  
Shiyang River Basin

The increase in the frequency and intensity of extreme weather events around the world has led to the frequent occurrence of global disasters, which have had serious impacts on the society, economic and ecological environment, especially fragile arid areas. Based on the daily maximum temperature and daily minimum temperature data of four meteorological stations in Shiyang River Basin (SRB) from 1960 to 2015, the spatio-temporal variation characteristics of extreme temperature indices were analyzed by means of univariate linear regression analysis, Mann–Kendall test and correlation analysis. The results showed that the extreme temperatures warming indices and the minimum of daily maximum temperature (TXn) and the minimum of daily minimum temperature (TNn) of cold indices showed an increasing trend from 1960 to 2016, especially since the 1990s, where the growth rate was fast and the response to global warming was sensitive. Except TXn and TNn, other cold indices showed a decreasing trend, especially Diurnal temperature (DTR) range, which decreased rapidly, indicating that the increasing speed of daily min-temperature were greater than of daily max-temperature in SRB. In space, the change tendency rate of the warm index basically showed an obvious altitude gradient effect that decreased with the altitude, which was consistent with Frost day (FD0) and Cool nights (TN10p) in the cold index, while Ice days (ID0) and Cool days (TX10p) are opposite. The mutation of the cold indices occurred earlier than the warm indices, illustrating that the cold indices in SRB were more sensitive to global warming. The change in extreme temperatures that would have a significant impact on the vegetation and glacier permafrost in the basin was the result of the combined function of different atmospheric circulation systems, which included the Arctic polar vortex, Western Pacific subtropical high and Qinghai-tibet Plateau circulation.

scholarly journals A climatology of polar stratospheric cloud composition between 2002 and 2012 based on MIPAS/Envisat observations

2018 ◽  
Vol 18 (7) ◽  
pp. 5089-5113 ◽  
Author(s):  
Reinhold Spang ◽  
Lars Hoffmann ◽  
Rolf Müller ◽  
Jens-Uwe Grooß ◽  
Ines Tritscher ◽  
...  
Keyword(s):  
Probability Density ◽  
Climate Models ◽  
Michelson Interferometer ◽  
European Space Agency ◽  
Equilibrium Temperature ◽  
Probability Density Functions ◽  
Solid Particles ◽  
Ratio Method ◽  
Orthogonal Polarization ◽  
Density Functions

Abstract. The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument aboard the European Space Agency (ESA) Envisat satellite operated from July 2002 to April 2012. The infrared limb emission measurements provide a unique dataset of day and night observations of polar stratospheric clouds (PSCs) up to both poles. A recent classification method for PSC types in infrared (IR) limb spectra using spectral measurements in different atmospheric window regions has been applied to the complete mission period of MIPAS. The method uses a simple probabilistic classifier based on Bayes' theorem with a strong independence assumption on a combination of a well-established two-colour ratio method and multiple 2-D probability density functions of brightness temperature differences. The Bayesian classifier distinguishes between solid particles of ice, nitric acid trihydrate (NAT), and liquid droplets of supercooled ternary solution (STS), as well as mixed types. A climatology of MIPAS PSC occurrence and specific PSC classes has been compiled. Comparisons with results from the classification scheme of the spaceborne lidar Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol-Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite show excellent correspondence in the spatial and temporal evolution for the area of PSC coverage (APSC) even for each PSC class. Probability density functions of the PSC temperature, retrieved for each class with respect to equilibrium temperature of ice and based on coincident temperatures from meteorological reanalyses, are in accordance with the microphysical knowledge of the formation processes with respect to temperature for all three PSC types. This paper represents unprecedented pole-covering day- and nighttime climatology of the PSC distributions and their composition of different particle types. The dataset allows analyses on the temporal and spatial development of the PSC formation process over multiple winters. At first view, a more general comparison of APSC and AICE retrieved from the observations and from the existence temperature for NAT and ice particles based on the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis temperature data shows the high potential of the climatology for the validation and improvement of PSC schemes in chemical transport and chemistry–climate models.

scholarly journals Response of shortwave cloud radiative effect to greenhouse gases and aerosols and its impact on daily maximum temperature

2020 ◽  
Author(s):  
Tao Tang ◽  
Drew Shindell ◽  
Yuqiang Zhang ◽  
Apostolos Voulgarakis ◽  
Jean-Francois Lamarque ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Maximum Temperature ◽  
Boreal Summer ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Sulfate Aerosols ◽  
Multilinear Regression

Abstract. Shortwave cloud radiative effects (SWCRE), defined as the difference of shortwave radiative flux between all-sky and clear-sky conditions, have been reported to play an important role in influencing the Earth’s energy budget and temperature extremes. In this study, we employed a set of global climate models to examine the SWCRE responses to CO2, black carbon (BC) aerosols and sulfate aerosols in boreal summer over the Northern Hemisphere. We found that CO2 causes positive SWCRE changes over most of the NH, and BC causes similar positive responses over North America, Europe and East China but negative SWCRE over India and tropical Africa. When normalized by effective radiative forcing, the SWCRE from BC is roughly 3–5 times larger than that from CO2. SWCRE change is mainly due to cloud cover changes resulting from the changes in relative humidity (RH) and, to a lesser extent, changes in circulation and stability. The SWCRE response to sulfate aerosols, however, is negligible compared to that for CO2 and BC. Using a multilinear regression model, it is found that mean daily maximum temperature (Tmax) increases by 0.15 K and 0.13 K per W m−2 increase in local SWCRE under the CO2 and BC experiment, respectively. When domain-averaged, the SWCRE change contribution to summer mean Tmax changes was 10–30 % under CO2 forcing and 30–50 % under BC forcing, varying by region, which can have important implications for extreme climatic events and socio-economic activities.

Detecting climate change in Ukraine: trends, prediction and extreme events

2021 ◽  
Author(s):  
Anna Bohushenko ◽  
Sergiy Stepanenko ◽  
Inna Khomenko
Keyword(s):  
Spatial Distribution ◽  
Minimum Temperature ◽  
Precipitation Amount ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Minimum Value ◽  
Maximum Value ◽  
Temperature Indices ◽  
Consecutive Dry Days

<p>In this study the trends and variations in 25 extreme temperature and precipitation indices<br>defined by ETCCDI, are examined using trend method, probability distribution analysis and<br>spatial statistics for periods of 71 to 137 years for 16 stations evenly distributed in Ukraine. Data<br>on the indices were obtained from www.ecad.eu.<br>Since 1981, temperature has increased by about 1ºC in all stations in question relative to the<br>period of 1945-1980. Analysis of the temperature indices indicates that during the 20th and the<br>beginning of the 21th century there is significant warming which is particularly pronounced in<br>annual mean and annual maximum temperatures. Occurrence of more summer days, warm days<br>and tropical nights and warm spell duration reached the record highest level, and conversely<br>occurrence of frost and ice days, cold days and cold spell duration fall to a record low for the last<br>three decades in the most of study territory.<br>Since 1981, precipitation amount has grown by 30-50 mm relative to the period of 1945-1980 for<br>the most of Ukrainian territory, except Uzhhorod and Uman where precipitation amount has<br>remained the same. For Ukraine average, an increase in maximum daily and maximum 5 days<br>precipitation amount, the maximum number of consecutive wet days, heavy and very heavy<br>precipitation days, and a decrease in the maximum number of consecutive dry days are observed<br>for the last three decades.<br>The analysis of the spatial distribution of trend of precipitation and temperature indices showed<br>that there are large differences between regions of Ukraine, and coherence of spatial distribution<br>of trends of various indices is low.<br>Spectral analysis and harmonic regression techniques were used to derive simulated and<br>predicted (2019-2050) values of annual precipitation and annual mean temperature and four<br>indices such as maximum value of daily maximum temperature, minimum value of daily<br>minimum temperature, the highest 1-day precipitation amount and maximum number of<br>consecutive dry days for some stations such as Kerch (the Crimean Peninsula), Kyiv (situated in<br>north-central Ukraine along the Dnieper River), Lubny (Dnieper Lowland), Lviv and Shepetivka<br>(Podillia Upland), Uzhhorod (Transcarpathia), Uman (Dnieper Upland).<br>Annual mean temperature and maximum value of daily maximum temperature were predicted to<br>increase by 0.33°C per decade in the period of 2019-2050 with respect to 1981-2018, while<br>minimum value of daily minimum temperature was predicted to grow slightly faster (by 0.43-<br>0.63ºC per decade).<br>Precipitation was predicted to increase for the stations in question by 20-66 mm up to 2050<br>relative to 1981-2018 and conversely maximum number of consecutive dry days will slightly<br>decline except Lubny where increase in an aridity index was predicted. In the next three decades<br>changes in maximum daily precipitation will be various: in Shepetivka and Kyiv such<br>precipitation will be decreased and in other stations increasement in such precipitation will be up<br>to 6 mm till 2050 with respect to 1981-2018.</p>

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