Long-Term Maximum and Minimum Temperature Projections Over Metro Vancouver, Canada

Evident climate change has been observed and projected in observation records and General Circulation Models (GCMs), respectively. This change is expected to reshape current seasonal variability; the degree varies between regions. High-resolution climate projections are thereby necessary to support further regional impact assessment. In this study, a gated recurrent unit-based recurrent neural network statistical downscaling model is developed to project future temperature change (both daily maximum temperature and minimum temperature) over Metro Vancouver, Canada. Three indexes (i.e., coefficient of determinant, root mean square error, and correlation coefficient) are estimated for model validation, indicating the developed model’s competitive ability to simulate the regional climatology of Metro Vancouver. Monthly comparisons between simulation and observation also highlight the effectiveness of the proposed downscaling method. The projected results (under one model set-up, WRF-MPI-ESM-LR, RCP 8.5) show that both maximum and minimum temperature will consistently increase between 2,035 and 2,100 over the 12 selected meteorological stations. By the end of this century, the daily maximum temperature and minimum temperature are expected to increase by an average of 2.91°C and 2.98°C. Nevertheless, with trivial increases in summer and significant rises in winter and spring, the seasonal variability will be reduced substantially, which indicates less energy requirement over Metro Vancouver. This is quite favorable for Metro Vancouver to switch from fossil fuel-based energy sources to renewable and clean forms of energy. Further, the cold extremes’ frequency of minimum temperature will be reduced as expected; however, despite evident warming trend, the hot extremes of maximum temperature will become less frequent.

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

Plant Disease ◽

10.1094/pdis.1998.82.1.26 ◽

1998 ◽

Vol 82 (1) ◽

pp. 26-29 ◽

Cited By ~ 17

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.

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A Statistical Adjustment of Regional Climate Model Outputs to Local Scales: Application to Platja de Palma, Spain

Journal of Climate ◽

10.1175/jcli-d-10-05024.1 ◽

2012 ◽

Vol 25 (3) ◽

pp. 939-957 ◽

Cited By ~ 59

Author(s):

A. Amengual ◽

V. Homar ◽

R. Romero ◽

S. Alonso ◽

C. Ramis

Keyword(s):

Climate Change ◽

Regional Climate ◽

Distribution Functions ◽

Cumulative Distribution ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Climate Change Signal ◽

Daily Maximum ◽

Climate Projections ◽

Recent Past

Abstract Projections of climate change effects for the System of Platja de Palma (SPdP) are derived using a novel statistical technique. Socioeconomic activities developed in this settlement are very closely linked to its climate. Any planning for socioeconomic opportunities in the mid- and long term must take into account the possible effects of climate change. To this aim, daily observed series of minimum and maximum temperatures, precipitation, relative humidity, cloud cover, and wind speed have been analyzed. For the climate projections, daily data generated by an ensemble of regional climate models (RCMs) have been used. To properly use RCM data at local scale, a quantile–quantile adjustment has been applied to the simulated regional projections. The method is based on detecting changes in the cumulative distribution functions between the recent past and successive time slices of the simulated climate and applying these, after calibration, to the recent past (observed) series. Results show an overall improvement in reproducing the present climate baseline when using calibrated series instead of raw RCM outputs, although the correction does not result in such clear improvement when dealing with very extreme rainfalls. Next, the corrected series are analyzed to quantify the climate change signal. An increase of the annual means for temperatures together with a decrease for the remaining variables is projected throughout the twenty-first century. Increases in weak and intense daily rainfalls and in high extremes for daily maximum temperature can also be expected. With this information at hand, the experts planning the future of SPdP can respond more effectively to the problem of local adaptation to climate change.

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

Atmosphere ◽

10.3390/atmos11111171 ◽

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.

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

10.5194/egusphere-egu21-6197 ◽

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

In this study the trends and variations in 25 extreme temperature and precipitation indices defined by ETCCDI, are examined using trend method, probability distribution analysis and spatial statistics for periods of 71 to 137 years for 16 stations evenly distributed in Ukraine. Data on the indices were obtained from www.ecad.eu. Since 1981, temperature has increased by about 1&#186;C in all stations in question relative to the period of 1945-1980. Analysis of the temperature indices indicates that during the 20th and the beginning of the 21th century there is significant warming which is particularly pronounced in annual mean and annual maximum temperatures. Occurrence of more summer days, warm days and tropical nights and warm spell duration reached the record highest level, and conversely occurrence of frost and ice days, cold days and cold spell duration fall to a record low for the last three decades in the most of study territory. Since 1981, precipitation amount has grown by 30-50 mm relative to the period of 1945-1980 for the most of Ukrainian territory, except Uzhhorod and Uman where precipitation amount has remained the same. For Ukraine average, an increase in maximum daily and maximum 5 days precipitation amount, the maximum number of consecutive wet days, heavy and very heavy precipitation days, and a decrease in the maximum number of consecutive dry days are observed for the last three decades. The analysis of the spatial distribution of trend of precipitation and temperature indices showed that there are large differences between regions of Ukraine, and coherence of spatial distribution of trends of various indices is low. Spectral analysis and harmonic regression techniques were used to derive simulated and predicted (2019-2050) values of annual precipitation and annual mean temperature and four indices such as maximum value of daily maximum temperature, minimum value of daily minimum temperature, the highest 1-day precipitation amount and maximum number of consecutive dry days for some stations such as Kerch (the Crimean Peninsula), Kyiv (situated in north-central Ukraine along the Dnieper River), Lubny (Dnieper Lowland), Lviv and Shepetivka (Podillia Upland), Uzhhorod (Transcarpathia), Uman (Dnieper Upland). Annual mean temperature and maximum value of daily maximum temperature were predicted to increase by 0.33&#176;C per decade in the period of 2019-2050 with respect to 1981-2018, while minimum value of daily minimum temperature was predicted to grow slightly faster (by 0.43- 0.63&#186;C per decade). Precipitation was predicted to increase for the stations in question by 20-66 mm up to 2050 relative to 1981-2018 and conversely maximum number of consecutive dry days will slightly decline except Lubny where increase in an aridity index was predicted. In the next three decades changes in maximum daily precipitation will be various: in Shepetivka and Kyiv such precipitation will be decreased and in other stations increasement in such precipitation will be up to 6 mm till 2050 with respect to 1981-2018.

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UK Climate Projections 2018 (UKCP18): progress towards more information on future weather

10.5194/egusphere-egu2020-7297 ◽

2020 ◽

Author(s):

David Sexton ◽

Jason Lowe ◽

James Murphy ◽

Glen Harris ◽

Elizabeth Kendon ◽

...

Keyword(s):

Large Scale ◽

Future Climate ◽

Maximum Temperature ◽

Cmip5 Models ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Climate Projections ◽

Circulation Index ◽

Large Contribution ◽

Probabilistic Projections

UK Climate Projections 2018 (UKCP18) included land and marine projections and were published in 2018 to replace UKCP09. The land projections had three components, and all were designed to provide more information on future weather compared to UKCP09. The first component updated the UKCP09 probabilistic projections by including newer CMIP5 data and focussing on seasonal means from individual years rather than 30-year averages. The probabilistic projections represent the wider uncertainty. The second two components (global and regional projections) both had the aim of providing plausible examples of future climate, but at different resolutions.The global projections were a combination of 13 CMIP5 models and a 15-member perturbed parameter ensemble (PPE) of coupled simulations for 1900-2100 using CMIP5 RCP8.5 from 2005 onwards. The PPE was provided at 60km atmosphere, quarter degree ocean and the large-scale conditions from twelve of the members were used to drive the regional model at both 12km and 2.2km resolution. These plausible examples are more useful for providing information about weather in a future climate to support a storyline approach for decision making.The talk will present examples of new ways to use UKCP18 compared to UKCP09.&#160; We will show how the global projections can be used to understand that the recent record winter daily maximum temperature in the UK in 2019 had a large contribution from internal variability and what this means for breaking the record in a warming climate. We also use an example from China to demonstrate one way to exploit information at different time scales, looking at how a circulation index, which is predictable and related to tropical cyclone landfall, changes in a future climate.Finally, we show that while the enhanced resolution of the global and regional projections has improved our capability to provide climate information linked to the better representation of circulation, they lack diversity in some of the key drivers of future climate. Therefore, a key way forward will be to find an appropriate balance between the need for better diversity (e.g. multiple ensembles such as CMIP or PPEs) and the need for an appropriate resolution to retain this new capability.

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Assessing the Spatial Distribution of Biodiversity in a Changing Temperature Pattern: The Case of Catalonia, Spain

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16204026 ◽

2019 ◽

Vol 16 (20) ◽

pp. 4026 ◽

Cited By ~ 1

Author(s):

Diego Varga ◽

Mariona Roigé ◽

Josep Pintó ◽

Marc Saez

Keyword(s):

Land Use ◽

Minimum Temperature ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Temperature Pattern ◽

Explanatory Variables ◽

Number Of Species ◽

Protected Natural Areas ◽

Planning Processes

The impacts that climate change and land-use dynamics have on biodiversity are already visible in the distribution and behaviour of a large number of species. By using a Bayesian framework, including land-use, meteorological, topography and other variables as explanatory variables, such as distance to roads and urban centres, we modeled a number of species within each cell of a regular lattice for Catalonia, Spain, in the period of 2004 to 2010. We estimated a slight increase in daily maximum temperature and a more significant increase in minimum temperature (a 5-year increase of 0.159 °C in maximum temperature, and an increase of 0.332 °C in minimum temperature). The estimation shows that the total number of species was greater than expected in the cells where land use was not urban—38.4%, in forests and 55.2% in mixed forests. Finally, we observed that most invasive species are found in areas where the minimum temperature is expected to increase. Our study can help with making important recommendations as to where, when and how future threats could affect specie distribution and the kind of planning processes needed for when protected natural areas will be unable to continue to support all the species they were designed to protect.

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Biases and Model Agreement in Projections of Climate Extremes over the Tropical Pacific

Earth Interactions ◽

10.1175/2011ei395.1 ◽

2011 ◽

Vol 15 (24) ◽

pp. 1-36 ◽

Cited By ~ 18

Author(s):

Sarah E. Perkins

Keyword(s):

Minimum Temperature ◽

General Circulation ◽

Coupled Model ◽

Climate Extremes ◽

General Circulation Models ◽

Maximum Temperature ◽

Climate Indices ◽

Temperature And Precipitation ◽

Return Levels ◽

The Mean

Abstract Using the Coupled Model Intercomparison Project phase 3 (CMIP3) general circulation models (GCMs), projections of a range of climate extremes are explored for the western Pacific. These projections include the 1-in-20-yr return levels and a selection of climate indices for minimum temperature, maximum temperature, and precipitation, and they are compared to corresponding mean projections for the Special Report on Emission Scenarios (SRES) A2 scenario during 2081–2100. Models are evaluated per variable based on their ability to simulate current extremes, as well as the overall daily distribution. Using the standardized evaluation scores for each variable, models are divided into four subsets where ensemble variability is calculated to measure model uncertainty and biases are calculated in respect to the multimodel ensemble (MME). Results show that higher uncertainty in projections of climate extremes exists when compared to the mean, even in those subsets consisting of higher-skilled models. Higher uncertainty exists for precipitation projections than for temperature, and biases and uncertainties in the 1-in-20-yr precipitation events are an order of magnitude higher than the corresponding mean. Poorer performing models exhibit a cooler bias in the mean and 1-in-20-yr return levels for maximum and minimum temperature, and ensemble variability is low among all subsets of mean minimum temperature, especially the lower-skilled subsets. Higher-skilled models project 1-in-20-yr precipitation return levels that are more intense than in the MME. The frequency of temperature extremes increase dramatically; however, this is explained by the underpinning small temperature range of the region. Although some systematic biases occur in the higher- and lower-skilled models and omitting the poorer performers is recommended, great care should be exercised when interpreting the reduction of uncertainty because the ensemble variability among the remaining models is comparable and in some cases greater than the MME. Such results should be treated on a case-by-case basis.

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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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Half-hourly changes in intertidal temperature at nine wave-exposed locations along the Atlantic Canadian coast: a 5.5-year study

10.5194/essd-2020-161 ◽

2020 ◽

Author(s):

Ricardo A. Scrosati ◽

Julius A. Ellrich ◽

Matthew J. Freeman

Keyword(s):

Minimum Temperature ◽

High Tide ◽

Thermal Conditions ◽

Maximum Temperature ◽

Daily Minimum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Measured Temperature ◽

Data Set ◽

Intertidal Habitats

Abstract. Intertidal habitats are unique because they spend alternating periods of submergence (at high tide) and emergence (at low tide) every day. Thus, intertidal temperature is mainly driven by sea surface temperature (SST) during high tides and by air temperature during low tides. Because of that, the switch from high to low tides and viceversa can determine rapid changes in intertidal thermal conditions. On cold-temperate shores, which are characterized by cold winters and warm summers, intertidal thermal conditions can also change considerably with seasons. Despite this uniqueness, knowledge on intertidal temperature dynamics is more limited than for open seas. This is especially true for wave-exposed intertidal habitats, which, in addition to the unique properties described above, are also characterized by wave splash being able to moderate intertidal thermal extremes during low tides. To address this knowledge gap, we measured temperature every half hour during a period of 5.5 years (2014–2019) at nine wave-exposed rocky intertidal locations along the Atlantic coast of Nova Scotia, Canada. This data set is freely available from the figshare online repository (Scrosati and Ellrich, 2020a; https://doi.org/10.6084/m9.figshare.12462065.v1). We summarize the main properties of this data set by focusing on location-wise values of daily maximum and minimum temperature and daily SST, which we make freely available as a separate data set in figshare (Scrosati et al., 2020; https://doi.org/10.6084/m9.figshare.12453374.v1). Overall, this cold-temperate coast exhibited a wide annual SST range, from a lowest overall value of −1.8 °C in winter to a highest overall value of 22.8 °C in summer. In addition, the latitudinal SST trend along this coast experienced a reversal from winter, when SST increased southwards, to summer, when SST decreased southwards, seemingly driven by alongshore differences in coastal upwelling. Daily temperature maxima and minima were more extreme, as expected from their occurrence during low tides, ranging from a lowest overall value of −16.3 °C in winter to a highest overall value of 41.2 °C in summer. Daily maximum temperature in summer varied little along the coast, while daily minimum temperature in winter increased southwards. This data set is the first of its kind for the Atlantic Canadian coast and exemplifies in detail how intertidal temperature varies in wave-exposed environments on a cold-temperate coast.

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

Journal of Climate ◽

10.1175/jcli4253.1 ◽

2007 ◽

Vol 20 (17) ◽

pp. 4356-4376 ◽

Cited By ~ 381

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.

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