scholarly journals Effects of Climate Change on Precipitation and the Maximum Daily Temperature (Tmax) at Two US Military Bases with Different Present-Day Climates

Climate ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 18
Author(s):  
Jovan Tadić ◽  
Sébastien Biraud
Keyword(s):  
Climate Change ◽  
North Carolina ◽  
Emission Scenario ◽  
Daily Temperature ◽  
Military Bases ◽  
Climate Change Signal ◽  
Maximum Daily Temperature ◽  
Us Military ◽  
Cross Covariance ◽  
Tail Distributions

In this study, the effects of climate change on precipitation and the maximum daily temperature (Tmax) at two USA locations that have different climates—the Travis Airforce Base (AFB) in California [38.27° N, 121.93° W] and Fort Bragg (FBR) in North Carolina [35.14 N, 79.00 W]—are analyzed. The effects of climate change on central tendency, tail distributions, and both auto- and cross-covariance structures in precipitation and Tmax fields for three time periods in the 21st century centered on the years 2020, 2050, and 2100 were analyzed. It was found that, on average, Tmax under the Representative Concentration Pathway (RCP) 4.5 emission scenario is projected to increase for the years 2020, 2050, and 2100 by 1.1, 2.0, and 2.2 °C, respectively, for AFB, and 0.9, 1.2, and 1.6 °C, respectively, for FBR, while under the RCP8.5 emission scenario Tmax will increase by 1.1, 1.9, and 2.7 °C, respectively, for AFB, and 0.1, 1.5, and 2.2 °C, respectively, for FBR. The climate change signal in precipitation is weak. The results show that, under different emission scenarios, events considered to be within 1% of the most extreme events in the past will become ~13–30 times more frequent for Tmax, ~and 0.05–3 times more frequent for precipitation in both locations. Several analytical methods were deployed in a sequence, creating an easily scalable framework for similar analyses in the future.

Subsampling Impact on the Climate Change Signal over Poland Based on Simulations from Statistical and Dynamical Downscaling

2019 ◽  
Vol 58 (5) ◽  
pp. 1061-1078 ◽  
Author(s):  
Abdelkader Mezghani ◽  
Andreas Dobler ◽  
Rasmus Benestad ◽  
Jan Erik Haugen ◽  
Kajsa M. Parding ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Emission Scenario ◽  
Dynamical Downscaling ◽  
Full Range ◽  
Global Climate Models ◽  
Climate Change Signal ◽  
High Emission ◽  
Selection Of

ABSTRACTMost impact studies using downscaled climate data as input assume that the selection of few global climate models (GCMs) representing the largest spread covers the likely range of future changes. This study shows that including more GCMs can result in a very different behavior. We tested the influence of selecting various subsets of GCMs on the climate change signal over Poland from simulations based on dynamical and empirical–statistical downscaling methods. When the climate variable is well simulated by the GCM, such as temperature, results showed that both downscaling methods agree on a warming over Poland by up to 2° or 5°C assuming intermediate or high emission scenarios, respectively, by 2071–2100. As a less robust simulated signal through GCMs, precipitation is expected to increase by up to 10% by 2071–2100 assuming the intermediate emission scenario. However, these changes are uncertain when the high emission scenario and the end of the twenty-first century are of interest. Further, an additional bootstrap test revealed an underestimation in the warming rate varying from 0.5° to more than 4°C over Poland that was found to be largely influenced by the selection of few driving GCMs instead of considering the full range of possible climate model outlooks. Furthermore, we found that differences between various combinations of small subsets from the GCM ensemble of opportunities can be as large as the climate change signal.

Assessing several downscaling methods for daily minimum and maximum temperature in a mountainous area. Are we able to statistically simulate a warmer climate in the Pyrenees?

2020 ◽  
Author(s):  
Marc Lemus-Canovas ◽  
Swen Brands
Keyword(s):  
Climate Change ◽  
Decision Making ◽  
Regression Models ◽  
Temporal Correlation ◽  
Daily Temperature ◽  
Maximum Temperature ◽  
Mountainous Area ◽  
Present Contribution ◽  
Mountain Areas ◽  
Maximum Daily Temperature

<p>Mountain areas are one of the most vulnerable areas to climate change, due to the large amount of natural resources they contribute to society. Moreover, the announced increase in temperature for the next few decades may have uncertain consequences for the ecosystems and landscapes of such territories. To face this challenge, it is necessary to test the capacity to simulate the climate of warm periods using observed data. In the present contribution, different perfect prog (PP) downscaling methods were evaluated to simulate the minimum and maximum daily temperature in a 1x1 km grid in the Pyrenees (Spain, France & Andorra) for the period 1985-2015. To obtain the results, several combinations of predictors, different geographical domains of such predictors, as well as different reanalysis databases were used, to check how much they can influence the prediction skill. In addition, different metrics were calculated to evaluate the bias, the similarity in the observed and predicted distributions, the temporal correlation, etc.</p><p>The results obtained reflect that the regression models better represent the warm periods using the observed data, as well as a lower bias. The present study will facilitate the decision making on which method of downscaling PP is more useful to reproduce the future temperature in the Pyrenees.</p><p> </p><p><strong>Keywords: </strong>Statistical downscaling, perfect prog, Pyrenees, daily temperature.</p>

scholarly journals Impact of air–sea coupling on the climate change signal over the Iberian Peninsula

2021 ◽  
Author(s):  
Alba de la Vara ◽  
William Cabos ◽  
Dmitry V. Sein ◽  
Claas Teichmann ◽  
Daniela Jacob
Keyword(s):  
Climate Change ◽  
Iberian Peninsula ◽  
Mediterranean Sea ◽  
Large Scale ◽  
Regional Distribution ◽  
Coupled Model ◽  
Climate Change Signal ◽  
The North ◽  
The Mediterranean ◽  
The Impact

AbstractIn this work we use a regional atmosphere–ocean coupled model (RAOCM) and its stand-alone atmospheric component to gain insight into the impact of atmosphere–ocean coupling on the climate change signal over the Iberian Peninsula (IP). The IP climate is influenced by both the Atlantic Ocean and the Mediterranean sea. Complex interactions with the orography take place there and high-resolution models are required to realistically reproduce its current and future climate. We find that under the RCP8.5 scenario, the generalized 2-m air temperature (T2M) increase by the end of the twenty-first century (2070–2099) in the atmospheric-only simulation is tempered by the coupling. The impact of coupling is specially seen in summer, when the warming is stronger. Precipitation shows regionally-dependent changes in winter, whilst a drier climate is found in summer. The coupling generally reduces the magnitude of the changes. Differences in T2M and precipitation between the coupled and uncoupled simulations are caused by changes in the Atlantic large-scale circulation and in the Mediterranean Sea. Additionally, the differences in projected changes of T2M and precipitation with the RAOCM under the RCP8.5 and RCP4.5 scenarios are tackled. Results show that in winter and summer T2M increases less and precipitation changes are of a smaller magnitude with the RCP4.5. Whilst in summer changes present a similar regional distribution in both runs, in winter there are some differences in the NW of the IP due to differences in the North Atlantic circulation. The differences in the climate change signal from the RAOCM and the driving Global Coupled Model show that regionalization has an effect in terms of higher resolution over the land and ocean.

scholarly journals Anthropogenic climate change has changed frequency of past flood during 2010-2013

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yukiko Hirabayashi ◽  
Haireti Alifu ◽  
Dai Yamazaki ◽  
Yukiko Imada ◽  
Hideo Shiogama ◽  
...  
Keyword(s):  
Climate Change ◽  
South America ◽  
Radiative Forcing ◽  
Water Cycle ◽  
Flood Frequency ◽  
Anthropogenic Climate Change ◽  
Climate Change Signal ◽  
Northern Eurasia ◽  
Large Ensemble ◽  
The Impact

AbstractThe ongoing increases in anthropogenic radiative forcing have changed the global water cycle and are expected to lead to more intense precipitation extremes and associated floods. However, given the limitations of observations and model simulations, evidence of the impact of anthropogenic climate change on past extreme river discharge is scarce. Here, a large ensemble numerical simulation revealed that 64% (14 of 22 events) of floods analyzed during 2010-2013 were affected by anthropogenic climate change. Four flood events in Asia, Europe, and South America were enhanced within the 90% likelihood range. Of eight snow-induced floods analyzed, three were enhanced and four events were suppressed, indicating that the effects of climate change are more likely to be seen in the snow-induced floods. A global-scale analysis of flood frequency revealed that anthropogenic climate change enhanced the occurrence of floods during 2010-2013 in wide area of northern Eurasia, part of northwestern India, and central Africa, while suppressing the occurrence of floods in part of northeastern Eurasia, southern Africa, central to eastern North America and South America. Since the changes in the occurrence of flooding are the results of several hydrological processes, such as snow melt and changes in seasonal and extreme precipitation, and because a climate change signal is often not detectable from limited observation records, large ensemble discharge simulation provides insights into anthropogenic effects on past fluvial floods.

scholarly journals Atmospheric Climate Change Detection by Radio Occultation Data Using a Fingerprinting Method

2011 ◽  
Vol 24 (20) ◽  
pp. 5275-5291 ◽  
Author(s):  
Bettina C. Lackner ◽  
Andrea K. Steiner ◽  
Gabriele C. Hegerl ◽  
Gottfried Kirchengast
Keyword(s):  
Climate Change ◽  
Change Detection ◽  
Confidence Level ◽  
Geopotential Height ◽  
Radio Occultation ◽  
Lower Stratosphere ◽  
Quality Data ◽  
Climate Change Signal ◽  
High Quality ◽  
Climate Change Detection

Abstract The detection of climate change signals in rather short satellite datasets is a challenging task in climate research and requires high-quality data with good error characterization. Global Navigation Satellite System (GNSS) radio occultation (RO) provides a novel record of high-quality measurements of atmospheric parameters of the upper-troposphere–lower-stratosphere (UTLS) region. Because of characteristics such as long-term stability, self calibration, and a very good height resolution, RO data are well suited to investigate atmospheric climate change. This study describes the signals of ENSO and the quasi-biennial oscillation (QBO) in the data and investigates whether the data already show evidence of a forced climate change signal, using an optimal-fingerprint technique. RO refractivity, geopotential height, and temperature within two trend periods (1995–2010 intermittently and 2001–10 continuously) are investigated. The data show that an emerging climate change signal consistent with the projections of three global climate models from the Coupled Model Intercomparison Project cycle 3 (CMIP3) archive is detected for geopotential height of pressure levels at a 90% confidence level both for the intermittent and continuous period, for the latter so far in a broad 50°S–50°N band only. Such UTLS geopotential height changes reflect an overall tropospheric warming. 90% confidence is not achieved for the temperature record when only large-scale aspects of the pattern are resolved. When resolving smaller-scale aspects, RO temperature trends appear stronger than GCM-projected trends, the difference stemming mainly from the tropical lower stratosphere, allowing for climate change detection at a 95% confidence level. Overall, an emerging trend signal is thus detected in the RO climate record, which is expected to increase further in significance as the record grows over the coming years. Small natural changes during the period suggest that the detected change is mainly caused by anthropogenic influence on climate.

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.

Feasibility of The Olympic Marathon Under Climatic And Socioeconomic Change

2021 ◽  
Author(s):  
Takahiro Oyama ◽  
Jun'ya Takakura ◽  
Minoru Fujii ◽  
Kenichi Nakajima ◽  
Yasuaki Hijioka
Keyword(s):  
Climate Change ◽  
21St Century ◽  
Emission Scenario ◽  
Olympic Games ◽  
Climate Factors ◽  
Socioeconomic Conditions ◽  
Adaptation Measures ◽  
Marathon Runners ◽  
Late 21St Century ◽  
The Impact

Abstract There are concerns about the impact of climate change on Olympic Games, especially endurance events, such as marathons. In recent competitions, many marathon runners dropped out of their races due to extreme heat, and it is expected that more areas will be unable to host the Olympic Games due to climate change. Here, we show the feasibility of the Olympic marathon considering the variations in climate factors, socioeconomic conditions, and adaptation measures. The number of current possible host cities will decline by up to 24% worldwide by the late 21st century. Dozens of emerging cities, especially in Asia, will not be capable of hosting the marathon under the highest emission scenario. Moving the marathon from August to October and holding the games in multiple cities in the country are effective measures, and they should be considered if we are to maintain the regional diversity of the games.

On the added value of the regional climate model REMO in the assessment of climate change signal over Central Africa

2017 ◽  
Vol 49 (11-12) ◽  
pp. 3813-3838 ◽  
Author(s):  
Thierry C. Fotso-Nguemo ◽  
Derbetini A. Vondou ◽  
Wilfried M. Pokam ◽  
Zéphirin Yepdo Djomou ◽  
Ismaïla Diallo ◽  
...  
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Central Africa ◽  
Added Value ◽  
Climate Change Signal

scholarly journals Evaluating quantile-based bias adjustment methods for climate change scenarios

2021 ◽  
Author(s):  
Fabian Lehner ◽  
Imran Nadeem ◽  
Herbert Formayer
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Meteorological Data ◽  
Climate Change Signal ◽  
Historical Period ◽  
Climate Change Scenarios ◽  
Statistical Bias ◽  
Bias Adjustment ◽  
Direct Model

Abstract. Daily meteorological data such as temperature or precipitation from climate models is needed for many climate impact studies, e.g. in hydrology or agriculture but direct model output can contain large systematic errors. Thus, statistical bias adjustment is applied to correct climate model outputs. Here we review existing statistical bias adjustment methods and their shortcomings, and present a method which we call EQA (Empirical Quantile Adjustment), a development of the methods EDCDFm and PresRAT. We then test it in comparison to two existing methods using real and artificially created daily temperature and precipitation data for Austria. We compare the performance of the three methods in terms of the following demands: (1): The model data should match the climatological means of the observational data in the historical period. (2): The long-term climatological trends of means (climate change signal), either defined as difference or as ratio, should not be altered during bias adjustment, and (3): Even models with too few wet days (precipitation above 0.1 mm) should be corrected accurately, so that the wet day frequency is conserved. EQA fulfills (1) almost exactly and (2) at least for temperature. For precipitation, an additional correction included in EQA assures that the climate change signal is conserved, and for (3), we apply another additional algorithm to add precipitation days.

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