scholarly journals Multidimensional risk in a nonstationary climate: Joint probability of increasingly severe warm and dry conditions

2018 ◽  
Vol 4 (11) ◽  
pp. eaau3487 ◽  
Author(s):  
Ali Sarhadi ◽  
María Concepción Ausín ◽  
Michael P. Wiper ◽  
Danielle Touma ◽  
Noah S. Diffenbaugh
Keyword(s):  
Joint Probability ◽  
Climate Forcing ◽  
Climate Variables ◽  
Human Influence ◽  
Statistical Confidence ◽  
Emissions Mitigation ◽  
Multiple Regions ◽  
Dry Conditions ◽  
Critical Insight ◽  
Nonstationary Climate

We present a framework for quantifying the spatial and temporal co-occurrence of climate stresses in a nonstationary climate. We find that, globally, anthropogenic climate forcing has doubled the joint probability of years that are both warm and dry in the same location (relative to the 1961–1990 baseline). In addition, the joint probability that key crop and pasture regions simultaneously experience severely warm conditions in conjunction with dry years has also increased, including high statistical confidence that human influence has increased the probability of previously unprecedented co-occurring combinations. Further, we find that ambitious emissions mitigation, such as that in the United Nations Paris Agreement, substantially curbs increases in the probability that extremely hot years co-occur with low precipitation simultaneously in multiple regions. Our methodology can be applied to other climate variables, providing critical insight for a number of sectors that are accustomed to deploying resources based on historical probabilities.

Multidimensional risk in a nonstationary climate: changes in joint probability of extreme conditions in space and time

2021 ◽  
Author(s):  
Noah Diffenbaugh
Keyword(s):  
Disaster Preparedness ◽  
Climate Changes ◽  
Joint Probability ◽  
Climate Forcing ◽  
Severe Drought ◽  
Human Influence ◽  
Historical Experience ◽  
Statistical Confidence ◽  
Surge Flooding ◽  
Nonstationary Climate

<p>As has been made acutely clear in recent years, many natural and human systems are particularly prone to the co-occurrence of extremes like severe heat, heavy rainfall, storm-surge flooding, severe drought, and extreme wildfire conditions. The co-occurrence of these conditions, both simultaneously (or in rapid succession) in a given location or in different parts of the world, is critical for a broad suite of climate-sensitive concerns, including agricultural markets, food security, poverty vulnerability, supply chains, weather-related insurance and reinsurance, and disaster preparedness and recovery - particularly when those conditions are sufficiently extreme to fall outside of historical experience. This seminar will summarize recent work quantifying changes in the frequency of unprecedented events without consideration for joint probability probability, and then present a framework for quantifying the spatial and temporal co-occurrence of climate stresses in a nonstationary climate. This framework shows that, globally, anthropogenic climate forcing has doubled the joint probability of years that are both warm and dry in the same location (relative to the 1961–1990 baseline). In addition, the joint probability that key crop and pasture regions simultaneously experience severely warm conditions in conjunction with dry years has also increased, including high statistical confidence that human influence has increased the probability of previously unprecedented co-occurring combinations. The potential for this methodology to lend insight for other sectors that are accustomed to deploying resources based on historical probabilities, such as wildfire risk management, will also be discussed.</p>

The warm and extremely dry spring in 2015 in Tasmania contained the fingerprint of human influence on the climate

2019 ◽  
Vol 69 (1) ◽  
pp. 183
Author(s):  
Michael R. Grose ◽  
Mitchell T. Black ◽  
Guomin Wang ◽  
Andrew D. King ◽  
Pandora Hope ◽  
...  
Keyword(s):  
Drought Index ◽  
Human Influence ◽  
Sea Level Pressure ◽  
Positive Indian Ocean Dipole ◽  
Event Attribution ◽  
Warming Pattern ◽  
Dry Conditions ◽  
Recent Trends ◽  
Higher Temperature ◽  
Human Signal

Tasmania saw a warm and very dry spring and summer in 2015–16, including a record dry October, which had significant, wide-ranging impacts. A previous study using two probabilistic event-attribution techniques found a small but statistically significant increase in the likelihood of the record dry October due to anthropogenic influence. Given the human signal was less clear amid natural variability for rainfall compared to temperature extremes, here we provided further evidence and context for this finding. An additional attribution method supported the October rainfall finding, and the median attributable risk to human influence in the three methods was ~25%, 48% and 75%. The results suggested that human influence on rainfall was partly through increased sea level pressure in the mid-latitudes associated with fewer rainbearing systems, a circulation driver that was consistent with recent trends that have been attributed to human influence. Dry conditions were also driven by a positive Indian Ocean Dipole and El Niño at the time, but this study could not reliably estimate the effect of human influence on these phenomena, as each model gave a different estimate of the ocean warming pattern. Along with rainfall, attribution modelling showed a role for human influence in higher temperature and evaporation through October 2015, as well as a high drought index throughout spring. Confidence in the attribution of a human signal on this extreme dry event increased as multiple attribution methods agreed, a plausible atmospheric circulation driver was identified, and temperature and evaporation also showed an anthropogenic signal.

The effect of human influence on wet and dry European summers

2021 ◽  
Author(s):  
Nikolaos Christidis ◽  
Peter Stott
Keyword(s):  
Climate Change ◽  
Potential Evapotranspiration ◽  
Rainfall Variability ◽  
Climatic Conditions ◽  
Drought Indices ◽  
Human Influence ◽  
The North ◽  
Detection And Attribution ◽  
Standardised Precipitation Index ◽  
Dry Conditions

<p>As the climate becomes warmer under the influence of anthropogenic forcings, increases in the concentration of the atmospheric water vapour may lead to an intensification of wet and dry extremes. Understanding regional hydroclimatic changes can provide actionable information to help communities adapt to impacts specific to their location. This study employs an ensemble of 9 CMIP6 models and compares experiments with and without the effect of human influence using detection and attribution methodologies. The analysis employs two popular drought indices: the rainfall-based standardised precipitation index (SPI), and its extension, the standardized precipitation evapotranspiration index (SPEI), which also accounts for changes in potential evapotranspiration. Both indices are defined relative to the pre-industrial climate, which enables a comparison between past, present and future climatic conditions. Potential evapotranspiration is computed with the simple, temperature-based, Thornthwaite formula. The latter has been criticised for omitting the influences of radiation, humidity and wind, but has been shown to yield very similar trends, spatial averages and correlations with more sophisticated models. It is therefore deemed to be adequate in studies assessing the broader overall effect of climate change, which are more concerned with wet and dry trends and changes in characteristics of extremes rather than the precise estimation of drought index values. The rainfall-based index suggests a shift towards wetter conditions in the north and dryer in the south of the continent, as well as an overall increase in variability. Nevertheless, when the temperature effect is included, the wet trends in the north are largely masked leading to increasingly drier summers across most of the continent. A formal statistical methodology indicates that the fingerprint of forced climate change has emerged above variability and is thus detectable in the observational trends of both indices. A broadening of the SPI distribution also suggests higher rainfall variability in a warmer climate. The study demonstrates a striking drying trend in the Mediterranean region, suggesting that what were extremely dry conditions there in the pre-industrial climate may become normal by the end of the century.</p>

scholarly journals Achieving Climate Change Absolute Accuracy in Orbit

2013 ◽  
Vol 94 (10) ◽  
pp. 1519-1539 ◽  
Author(s):  
Bruce A. Wielicki ◽  
D. F. Young ◽  
M. G. Mlynczak ◽  
K. J. Thome ◽  
S. Leroy ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Climate Variables ◽  
Absolute Accuracy ◽  
Calibration Laboratory ◽  
Statistical Confidence ◽  
Uncertainty Sources ◽  
The Earth ◽  
Wide Range ◽  
Decadal Climate

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission will provide a calibration laboratory in orbit for the purpose of accurately measuring and attributing climate change. CLARREO measurements establish new climate change benchmarks with high absolute radiometric accuracy and high statistical confidence across a wide range of essential climate variables. CLARREO's inherently high absolute accuracy will be verified and traceable on orbit to Système Internationale (SI) units. The benchmarks established by CLARREO will be critical for assessing changes in the Earth system and climate model predictive capabilities for decades into the future as society works to meet the challenge of optimizing strategies for mitigating and adapting to climate change. The CLARREO benchmarks are derived from measurements of the Earth's thermal infrared spectrum (5–50 μm), the spectrum of solar radiation reflected by the Earth and its atmosphere (320–2300 nm), and radio occultation refractivity from which accurate temperature profiles are derived. The mission has the ability to provide new spectral fingerprints of climate change, as well as to provide the first orbiting radiometer with accuracy sufficient to serve as the reference transfer standard for other space sensors, in essence serving as a “NIST [National Institute of Standards and Technology] in orbit.” CLARREO will greatly improve the accuracy and relevance of a wide range of space-borne instruments for decadal climate change. Finally, CLARREO has developed new metrics and methods for determining the accuracy requirements of climate observations for a wide range of climate variables and uncertainty sources. These methods should be useful for improving our understanding of observing requirements for most climate change observations.

Chaos and Predictability of Climate

1997 ◽  
Vol 07 (12) ◽  
pp. 2881-2890 ◽  
Author(s):  
Sultan Hameed ◽  
Minghua Zhang
Keyword(s):  
Atmospheric Model ◽  
Atmospheric Dynamics ◽  
Time Dependent ◽  
Climate Forcing ◽  
Statistical Characteristics ◽  
External Forcing ◽  
Climate Variables ◽  
Chaotic Nature ◽  
Atmospheric Variations ◽  
Chaotic Parameter

While day-to-day atmospheric fluctuations are not predictable after two weeks or so because of the chaotic nature of the atmospheric dynamics, there is evidence that certain aspects of the atmospheric circulation are predictable on time scales much longer than two weeks in the presence of a time dependent climate forcing. This paper illustrates distinctions between weather predictability and climate predictability by using a truncated atmospheric model in an explicitly chaotic parameter regime. An external forcing with time scale longer than the weather tends to modulate the statistical characteristics of the chaotic atmospheric variations to enhance the predictability of time-averaged climate states. Moreover, there exist climate variables which are more predictable than others.

Dust storm frequency in relation to extreme droughts in arid regions of Iran

2021 ◽  
Author(s):  
reza modarres ◽  
Poria Mohit Esfahani
Keyword(s):  
Dust Storm ◽  
Arid Regions ◽  
Standardized Precipitation Index ◽  
Joint Probability ◽  
Climatic Conditions ◽  
Dust Storms ◽  
Drought Event ◽  
Drought Condition ◽  
Copula Functions ◽  
Dry Conditions

<p>Dust storms are frequent phenomenon in arid and semi arid regions of Iran which cover near 60 percent of the entire country in the center of Iran. Due to geographic and climatic conditions of prolonged dry conditions as well as poor land use management, dust storms occur in almost all seasons across the region. Drought is a major fator affecting the likelihood of dust storm occurrence across arid regions of Iran. We develop copula functions to investigate the effect of drought on dust storm frequency.The standardized precipitation Index (SPI) was caluclated and drought condition was defined based on SPI< -0.5. Dought severity and duration for each drought event were ca;culated and the number of dust days in each drought event ws also identified. The Archimedean copula families shoed that the probability of dust occurrence has a significant relationship to extreme drought conditions. The joint probability is then used to derive the joint return period of dust storms in relation to drought condition.</p>

scholarly journals Measured and predicted aerosol light scattering enhancement factors at the high alpine site Jungfraujoch

2010 ◽  
Vol 10 (5) ◽  
pp. 2319-2333 ◽  
Author(s):  
R. Fierz-Schmidhauser ◽  
P. Zieger ◽  
M. Gysel ◽  
L. Kammermann ◽  
P. F. DeCarlo ◽  
...  
Keyword(s):  
Light Scattering ◽  
Radiative Forcing ◽  
Single Scattering ◽  
Important Variable ◽  
Single Scattering Albedo ◽  
Climate Forcing ◽  
Ambient Relative Humidity ◽  
Enhancement Factors ◽  
Dry Conditions ◽  
Aerosol Type

Abstract. Ambient relative humidity (RH) determines the water content of atmospheric aerosol particles and thus has an important influence on the amount of visible light scattered by particles. The RH dependence of the particle light scattering coefficient (σsp) is therefore an important variable for climate forcing calculations. We used a humidification system for a nephelometer which allows for the measurement of σsp at a defined RH in the range of 20–95%. In this paper we present measurements of light scattering enhancement factors f(RH)=σsp(RH)/σsp(dry) from a 1-month campaign (May 2008) at the high alpine site Jungfraujoch (3580 m a.s.l.), Switzerland. Measurements at the Jungfraujoch are representative for the lower free troposphere above Central Europe. For this aerosol type hardly any information about the f(RH) is available so far. At this site, f(RH=85%) varied between 1.2 and 3.3. Measured f(RH) agreed well with f(RH) calculated with Mie theory using measurements of the size distribution, chemical composition and hygroscopic diameter growth factors as input. Good f(RH) predictions at RH<85% were also obtained with a simplified model, which uses the Ångström exponent of σsp(dry) as input. RH influences further intensive optical aerosol properties. The backscatter fraction decreased by about 30% from 0.128 to 0.089, and the single scattering albedo increased on average by 0.05 at 85% RH compared to dry conditions. These changes in σsp, backscatter fraction and single scattering albedo have a distinct impact on the radiative forcing of the Jungfraujoch aerosol.

scholarly journals The effect of climate on Burgundy vintage quality rankings

OENO One ◽  
2019 ◽  
Vol 53 (1) ◽  
Author(s):  
Robert E. Davis ◽  
Ruth A. Dimon ◽  
Gregory V. Jones ◽  
Benjamin Bois
Keyword(s):  
Diurnal Temperature Range ◽  
Growing Season ◽  
Climatic Factor ◽  
Bud Break ◽  
Climate Variables ◽  
Phenological Stage ◽  
Diurnal Temperature ◽  
Temperature Range ◽  
Dry Conditions ◽  
Break Period

Aim: Based on consensus rankings from prominent rating authorities, we examined the importance of a suite of climatic variables, organized by winegrape phenological stage, in distinguishing between high- and low-ranked vintages in Burgundy.Methods and Results: Vintage ratings of Burgundy wines acquired from 12 sources were evaluated to develop consensus rankings for red and white wines from 1961–2015. Climate variables (air temperature, precipitation, degree-day accumulations, etc.) were organized by mean phenological stage and compared between good and poor vintages using Mann-Whitney U tests and multivariate stepwise discriminant function analysis. High temperatures, particularly during the growing season, were found to be the most consistently important climatic factor in distinguishing good-quality vintages from poor-quality vintages. The best red vintages had a greater diurnal temperature range during the growing season, whereas the top white vintages were not distinguished by unusually warm conditions, but the bottom-ranked white vintages were particularly cool and wet. The impact of rainfall varied across the growing season, with top-ranked Burgundy wines benefitting from rainfall during the bud break period and dry conditions during the ripening phase.Conclusions: The most important climatic factor in distinguishing between top- and bottom-ranked vintages is growing season temperature, especially high diurnal temperature range (for reds) and high average maximum temperatures (for whites). Good Burgundy vintages are more likely when there is ample rainfall during the bud break period in April and dry conditions during the véraison and ripening phases.Significance and Impact of the Study: As viticulturalists adapt to regional climate trends, a better understanding of how specific climate variables affect wine quality becomes increasingly important in viticulture management.

Investigating the impact of different drought-heat signatures on carbon dynamics using a dynamic global vegetation model

2020 ◽  
Author(s):  
Elisabeth Tschumi ◽  
Sebastian Lienert ◽  
Karin van der Wiel ◽  
Fortunat Joos ◽  
Jakob Zscheischler
Keyword(s):  
Carbon Cycle ◽  
Heat Waves ◽  
Carbon Dynamics ◽  
Climate Forcing ◽  
Carbon Uptake ◽  
Climate Variables ◽  
Vegetation Model ◽  
Dynamic Global Vegetation Model ◽  
Global Vegetation

&lt;p&gt;&lt;span&gt;Droughts and heat waves have large impacts on the terrestrial carbon cycle. They lead to reductions in gross and net carbon uptake or anomalous increases in carbon emissions to the atmosphere because of responses such as stomatal closure, hydraulic failure and vegetation mortality. The impacts are particularly strong when drought and heat occur at the same time. Climate model simulations diverge in their occurrence frequency of compound hot and dry events, and it is unclear how these differences affect carbon dynamics. Furthermore, it is unknown whether an increase in frequency of droughts and heat waves leads to long-term changes in carbon dynamics, and how such an increase might affect vegetation composition.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;To study the immediate and long-term effects of varying signatures of droughts and heat waves on carbon dynamics such as inter-annual variability of carbon fluxes and cumulative carbon uptake, we employ the state-of-the-art dynamic global vegetation model LPX-Bern (v1.4) under different drought-heat scenarios.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;We have constructed five 100-yr long scenarios with different drought-heat signatures, representing a &amp;#8220;control&amp;#8221;, &amp;#8220;close to mean seasonal cycle&amp;#8221;, &amp;#8220;drought only&amp;#8221;, &amp;#8220;heat only&amp;#8221;, and &amp;#8220;compound drought and heat&amp;#8221; climate forcing to LPX-Bern. This is done by sampling daily climate variables from a 2000-year stationary simulation of a General Circulation Model (EC-Earth) for present-day climate conditions. Such a sampling ensures physically-consistent co-variability between climate variables in the climate forcing.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;We investigate the carbon-cycle response and changes in vegetation structure to different drought-heat signatures on a global grid, representing different land cover types and climate zones. Our results provide a better understanding of the links between hot and dry conditions and carbon dynamics. This may help to reduce uncertainties in carbon cycle projections, which is important for constraining carbon cycle-climate feedbacks.&lt;/span&gt;&lt;/p&gt;

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