Assessing air temperature trends in Mesoamerica and their implications for the future of horticulture

2016 ◽  
Vol 81 (2) ◽  
pp. 63-77 ◽  
Author(s):  
J.D.H. Keatinge ◽  
◽  
P. Imbach ◽  
D.R. Ledesma ◽  
J.d'A. Hughes ◽  
...  
Keyword(s):  
Air Temperature ◽  
Temperature Trends ◽  
The Future

scholarly journals An updated evaluation of the global mean land surface air temperature and surface temperature trends based on CLSAT and CMST

2021 ◽  
Vol 56 (1-2) ◽  
pp. 635-650 ◽  
Author(s):  
Qingxiang Li ◽  
Wenbin Sun ◽  
Xiang Yun ◽  
Boyin Huang ◽  
Wenjie Dong ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Temperature Trends ◽  
Global Mean

scholarly journals Cars as next generation mobile weather stations: an initial investigation

2021 ◽  
Author(s):  
Giuliano Andrea Pagani ◽  
Marcel Molendijk ◽  
Jan Willem Noteboom
Keyword(s):  
Air Temperature ◽  
Weather Conditions ◽  
Ground Temperature ◽  
Warning Systems ◽  
Traction Control ◽  
The Future ◽  
Weather Stations ◽  
Temporal And Spatial ◽  
Positive Correlation Coefficient ◽  
Digital Equipment

<p>Modern automobiles are becoming more and more “computers on the wheels” having lots of digital equipment on board. Such equipment is both for the comfort and entertainment of the passengers and for their safety. Sensors play a key role in measuring several parameters of the car performance (e.g., traction control, anti-lock breaking system) and also environmental  parameters are observed directly (e.g., air temperature) or can be somehow inferred (e.g., precipitation via windscreen wipers activity/speed).</p><p>KNMI has been provided air temperature recorded every 10 minutes by thousands of vehicles driving in the Netherlands for the period January-October 2020. We have performed an initial exploratory temporal and spatial analysis to understand the most promising periods of the day and areas where sufficient data is available to perform a more thorough data analysis in the future. Furthermore, we have performed a correlation analysis between the outside temperature measured by cars and air and ground temperature observed by official weather station sensors placed at one location on the Dutch highways. The correlation results for three randomly selected days (with different weather conditions) show a good positive correlation coefficient ranging from 0.93 to 0.76 for car and station air temperature and from 0.91 to 0.67 for car temperature and station ground temperature.</p><p>This initial exploration paves the way to the use of (OEM) car data as (mobile) weather stations. We foresee in the future to use a combination of sensed variables from cars such as air temperature, traction control, windscreen wipers activity for example to improve observations of road slipperiness and related warning systems that are not restricted to Dutch highways only.</p>

scholarly journals Historical and future temporal trends in the summer Urban Heat Island of Athens (Greece) 

2021 ◽  
Author(s):  
Tim van der Schriek ◽  
Konstantinos V. Varotsos ◽  
Dimitra Founda ◽  
Christos Giannakopoulos
Keyword(s):  
Climate Change ◽  
Air Pollution ◽  
Air Temperature ◽  
Temporal Trends ◽  
Temperature Data ◽  
Climate Change Scenarios ◽  
Heat Island ◽  
Hot Days ◽  
The Future ◽  
Urban Heat

<p>Historical changes, spanning 1971–2016, in the Athens Urban Heat Island (UHI) over summer were assessed by contrasting two air temperature records from established meteorological stations in urban and rural settings. When contrasting two 20-year historical periods (1976–1995 and 1996–2015), there is a significant difference in summer UHI regimes. The stronger UHI-intensity of the second period (1996–2015) is likely linked to increased pollution and heat input. Observations suggest that the Athens summer UHI characteristics even fluctuate on multi-annual basis. Specifically, the reduction in air pollution during the Greek Economic Recession (2008-2016) probable subtly changed the UHI regime, through lowering the frequencies of extremely hot days (T<sub>max</sub> > 37 °C) and nights (T<sub>min</sub> > 26 °C).</p><p>Subsequently, we examined the future temporal trends of two different UHIs in Athens (Greece) under three climate change scenarios. A five-member regional climate model (RCM) sub-ensemble from EURO-CORDEX with a horizontal resolution of 0.11° (~12 × 12 km) simulated air temperature data, spanning the period 1976–2100, for the two station sites. Three future emissions scenarios (RCP2.6, RCP4.5 and RCP8.5) were implanted in the simulations after 2005. The observed daily maximum and minimum air temperature data (T<sub>max</sub> and T<sub>min</sub>) from two historical UHI regimes (1976–1995 and 1996–2015, respectively) were used, separately, to bias-adjust the model simulations thus creating two sets of results.</p><p>This novel approach allowed us to assess future temperature developments in Athens under two different UHI intensity regimes. We found that the future frequency of days with T<sub>max</sub> > 37 °C in Athens was only different from rural background values under the intense UHI regime. There is a large increase in the future frequency of nights with T<sub>min</sub> > 26 °C in Athens under all UHI regimes and climate scenarios; these events remain comparatively rare at the rural site.</p><p>This study shows a large urban amplification of the frequency of extremely hot days and nights which is likely forced by increasing air pollution and heat input. Consequently, local mitigation policies aimed at decreasing urban atmospheric pollution are expected to be also effective in reducing urban temperatures during extreme heat events in Athens under all future climate change scenarios. Such policies therefore have multiple benefits, including: reducing electricity (energy) needs, improving living quality and decreasing heat- and pollution related illnesses/deaths.</p><p> </p>

Testing mean air temperature trends in southern Greece: a Bayesian approach

2021 ◽  
Author(s):  
Georgios Tsiotas ◽  
Anna Mamara ◽  
Athanassios Argiriou ◽  
Aikaterini (Katerina) Tsoukala
Keyword(s):  
Air Temperature ◽  
Bayesian Approach ◽  
Temperature Trends ◽  
Southern Greece

Snowpack energy balance and changes in the frequency and extremity of rain-on-snow events in the warming climate

2021 ◽  
Author(s):  
Ondrej Hotovy ◽  
Michal Jenicek
Keyword(s):  
Energy Balance ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Hydrological Cycle ◽  
Winter Season ◽  
Catchment Scale ◽  
The Future ◽  
Snow Water ◽  
The Impact ◽  
Catchment Runoff

<p>Seasonal snowpack significantly influences the catchment runoff and thus represents an important input for the hydrological cycle. Changes in the precipitation distribution and intensity, as well as a shift from snowfall to rain is expected in the future due to climate changes. As a result, rain-on-snow events, which are considered to be one of the main causes of floods in winter and spring, may occur more frequently. Heat from liquid precipitation constitutes one of the snowpack energy balance components. Consequently, snowmelt and runoff may be strongly affected by these temperature and precipitation changes.</p><p>The objective of this study is 1) to evaluate the frequency, inter-annual variability and extremity of rain-on-snow events in the past based on existing measurements together with an analysis of changes in the snowpack energy balance, and 2) to simulate the effect of predicted increase in air temperature on the occurrence of rain-on-snow events in the future. We selected 40 near-natural mountain catchments in Czechia with significant snow influence on runoff and with available long-time series (>35 years) of daily hydrological and meteorological variables. A semi-distributed conceptual model, HBV-light, was used to simulate the individual components of the water cycle at a catchment scale. The model was calibrated for each of study catchments by using 100 calibration trials which resulted in respective number of optimized parameter sets. The model performance was evaluated against observed runoff and snow water equivalent. Rain-on-snow events definition by threshold values for air temperature, snow depth, rain intensity and snow water equivalent decrease allowed us to analyze inter-annual variations and trends in rain-on-snow events during the study period 1965-2019 and to explain the role of different catchment attributes.</p><p>The preliminary results show that a significant change of rain-on-snow events related to increasing air temperature is not clearly evident. Since both air temperature and elevation seem to be an important rain-on-snow drivers, there is an increasing rain-on-snow events occurrence during winter season due to a decrease in snowfall fraction. In contrast, a decrease in total number of events was observed due to the shortening of the period with existing snow cover on the ground. Modelling approach also opened further questions related to model structure and parameterization, specifically how individual model procedures and parameters represent the real natural processes. To understand potential model artefacts might be important when using HBV or similar bucket-type models for impact studies, such as modelling the impact of climate change on catchment runoff.</p>

scholarly journals Contribution of SST change to multidecadal global and continental surface air temperature trends between 1910 and 2013

2019 ◽  
Vol 54 (3-4) ◽  
pp. 1295-1313
Author(s):  
Yidan Xu ◽  
Jianping Li ◽  
Cheng Sun ◽  
Xiaopei Lin ◽  
Hailong Liu ◽  
...  
Keyword(s):  
Air Temperature ◽  
North Atlantic ◽  
Surface Air Temperature ◽  
Western Pacific ◽  
Multidecadal Variability ◽  
The North ◽  
Temperature Trends ◽  
Community Atmosphere Model ◽  
Community Earth System Model ◽  
Goddard Institute

AbstractThe global mean surface air temperature (GMST) shows multidecadal variability over the period of 1910–2013, with an increasing trend. This study quantifies the contribution of hemispheric surface air temperature (SAT) variations and individual ocean sea surface temperature (SST) changes to the GMST multidecadal variability for 1910–2013. At the hemispheric scale, both the Goddard Institute for Space Studies (GISS) observations and the Community Earth System Model (CESM) Community Atmosphere Model 5.3 (CAM5.3) simulation indicate that the Northern Hemisphere (NH) favors the GMST multidecadal trend during periods of accelerated warming (1910–1945, 1975–1998) and cooling (1940–1975, 2001–2013), whereas the Southern Hemisphere (SH) slows the intensity of both warming and cooling processes. The contribution of the NH SAT variation to the GMST multidecadal trend is higher than that of the SH. We conduct six experiments with different ocean SST forcing, and find that all the oceans make positive contributions to the GMST multidecadal trend during rapid warming periods. However, only the Indian, North Atlantic, and western Pacific oceans make positive contributions to the GMST multidecadal trend between 1940 and 1975, whereas only the tropical Pacific and the North Pacific SSTs contribute to the GMST multidecadal trend between 2001 and 2013. The North Atlantic and western Pacific oceans have important impacts on modulating the GMST multidecadal trend across the entire 20th century. Each ocean makes different contributions to the SAT multidecadal trend of different continents during different periods.

Air temperature trends related to changes in atmospheric circulation in the wider area of Greece

2011 ◽  
Vol 32 (3) ◽  
pp. 737-750 ◽  
Author(s):  
P. T. Nastos ◽  
C. M. Philandras ◽  
D. Founda ◽  
C. S. Zerefos
Keyword(s):  
Atmospheric Circulation ◽  
Air Temperature ◽  
Temperature Trends ◽  
Of Greece

Identifying Rogue Air Temperature Stations Using Cluster Analysis of Percentile Trends

2005 ◽  
Vol 18 (8) ◽  
pp. 1275-1287 ◽  
Author(s):  
Scott M. Robeson ◽  
Jeffrey A. Doty
Keyword(s):  
Cluster Analysis ◽  
Air Temperature ◽  
Large Body ◽  
Large Temperature ◽  
Daily Maximum ◽  
Climatic Data ◽  
Single Linkage ◽  
Frequency Distributions ◽  
Temperature Trends ◽  
Average Linkage

Abstract A new and efficient method for identifying “rogue” air temperature stations—locations with unusually large air temperature trends—is presented. Instrumentation problems and spatially unrepresentative local climates are sometimes more apparent in air temperature extremes, yet can have more subtle impacts on variations in mean air temperature. As a result, using data from over 1300 stations in North America, the tails of daily air temperature frequency distributions were examined for unusual trends. In particular, linear trends in the 5th percentile of daily minimum air temperature during the winter months and the 95th percentile of daily maximum air temperature during the summer were analyzed. Cluster analysis then was used to identify stations that were distinct from other locations. Both single- and average linkage clustering were evaluated. By identifying individual stations along the entire periphery of the percentile trend space, single-linkage clustering appears to produce better results than that of average linkage. Average linkage clustering tends to group together several stations with large trends; however, only a handful of these stations appear distinctly different from the large body of trends toward the center of the percentile trend space. Maps of the rogue stations show that most are in close proximity to numerous other stations that were not grouped into the rogue cluster, making it unlikely that the unusually large temperature trends were due to regional climatic variations. As with all approaches for evaluating data quality, time series plots and station history information also must be inspected to more fully understand inhomogeneous variations in historical climatic data.

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