This document analyzes human and natural influences on changing atmospheric temperature patterns based on climate model simulations and satellite observations. The key findings are:
1) Both climate model simulations including human factors and satellite data show widespread tropospheric warming and stratospheric cooling over the past several decades.
2) These temperature changes are unlikely to be due to internal variability or natural external factors alone, based on comparisons to model runs with only natural forcings.
3) The observed temperature pattern matches that expected from increased greenhouse gases more closely than patterns from alternative forcings, providing evidence of a human influence on atmospheric temperatures.
Climate science part 3 - climate models and predicted climate changeLPE Learning Center
Many lines of evidence, from ice cores to marine deposits, indicate that Earth’s temperature, sea level, and distribution of plant and animal species have varied substantially throughout history. Ice cores from Antarctica suggest that over the past 400,000 years global temperature has varied as much as 10 degrees Celsius through ice ages and periods warmer than today. Before human influence, natural factors (such as the pattern of earth’s orbit and changes in ocean currents) are believed to be responsible for climate changes. For more, visit: http://www.extension.org/69150
Climate science part 3 - climate models and predicted climate changeLPE Learning Center
Many lines of evidence, from ice cores to marine deposits, indicate that Earth’s temperature, sea level, and distribution of plant and animal species have varied substantially throughout history. Ice cores from Antarctica suggest that over the past 400,000 years global temperature has varied as much as 10 degrees Celsius through ice ages and periods warmer than today. Before human influence, natural factors (such as the pattern of earth’s orbit and changes in ocean currents) are believed to be responsible for climate changes. For more, visit: http://www.extension.org/69150
Modeling the Climate System: Is model-based science like model-based engineer...Steve Easterbrook
Keynote Talk given at the ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (Models 2015), Ottawa, September 2015.
Development of an Integrated Urban Heat Island Simulation ToolSryahwa Publications
Urban heat island (UHI) effect is quite common in megacities due to the built-up area and reduced greenery coverage of land surface, which highly affect urban livability. An integrated urban heat island simulation tool is developed by accounting for major heat sources and heat sinks in selected area of interest, and their interactions with the surrounding environment.
Global Warming and the Sudan: Variation of Air Temperature over Sudan and So...Sryahwa Publications
Annual mean air temperature for Sudan and South Sudan for the periods 1961- 1990 and 1981-2010 was obtained as normal data for 19 stations for each period from Sudan Meteorological Authority and analyzed for variation with the course of time and for correlations between mean, maximum and minimum air temperature on the one hand and latitudes, longitudes and elevations on the other hand.
This is the 7th lesson the course - Climate Change & Global Environment taught at the Faculty of Social Sciences and Humanities of the Rajarata University of Sri Lanka
Meridional brightness temperatures were measured on the surface of Titan during the 2004–2014 portion of the
Cassini mission by the Composite Infrared Spectrometer. Temperatures mapped from pole to pole during five twoyear
periods show a marked seasonal dependence. The surface temperature near the south pole over this time
decreased by 2 K from 91.7±0.3 to 89.7±0.5 K while at the north pole the temperature increased by 1 K from
90.7±0.5 to 91.5±0.2 K. The latitude of maximum temperature moved from 19 S to 16 N, tracking the subsolar
latitude. As the latitude changed, the maximum temperature remained constant at 93.65±0.15 K. In 2010
our temperatures repeated the north–south symmetry seen by Voyager one Titan year earlier in 1980. Early in the
mission, temperatures at all latitudes had agreed with GCM predictions, but by 2014 temperatures in the north were
lower than modeled by 1 K. The temperature rise in the north may be delayed by cooling of sea surfaces and moist
ground brought on by seasonal methane precipitation and evaporation.
Modification and Climate Change Analysis of surrounding Environment using Rem...iosrjce
This review is presented in three parts. The first part explains such terms as climate, climate change,
climate change adaptation, remote sensing (RS) and geographical information systems (GIS). The second part
highlights some areas where RS and GIS are applicable in climate change analysis and adaptation. Issues
considered are snow/glacier monitoring, land cover monitoring, carbon trace/accounting, atmospheric
dynamics, terrestrial temperature monitoring, biodiversity conservation, ocean and coast monitoring, erosion
monitoring and control, agriculture, flood monitoring, health and disease, drought and desertification. The
third part concludes from all illustrated instances that climate change problems will be less understood and
managed without the application of RS and GIS. While humanity is still being plagued by climate change effects,
RS and GIS play a crucial role in its management for continued human survival. Key words: Climate, Climate
Change, Climate Change Adaptation, Geographical Information System and Remote Sensing.
4 Review on Different Evapotranspiration Empirical EquationsINFOGAIN PUBLICATION
For optimal design and management of hydrologic balance and scheduling irrigation models, the need to measure Evapotranspiration is of great importance. It helps in predicting when and how much water is required for any particular irrigation scheme. Reference Evapotranspiration is a standard nomenclature defined by FAO to provide a reference frame although it is not a full proof equation. Several scientists have developed multiple equations based of three primary directions viz. temperature based methods, radiation based methods and mass – transfer methods. Here in this paper, we have carried out a review on most of the popular equations and the objective is to elucidate the advantages and drawbacks each one of them register when put into use. The reference equation for standardization considered here is FAO 56 Penman Montheith equation. Thirty other equations from the three schools have been analysed here. Statistical Regression Analysis methods and coefficient of determination (R2), Root Mean Square Error (RMSE) and index of agreement (d) are the analytical parameters those are to be used while estimating their acceptance in evaluating the throughputs
From our climate panel in Grand Junction on August 4:
Our Forest, Our Water, Our Land: Local Impacts on Climate Change. Sponsored by Conservation Colorado, Mesa County Library, Math & Science Center
Modeling the Climate System: Is model-based science like model-based engineer...Steve Easterbrook
Keynote Talk given at the ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (Models 2015), Ottawa, September 2015.
Development of an Integrated Urban Heat Island Simulation ToolSryahwa Publications
Urban heat island (UHI) effect is quite common in megacities due to the built-up area and reduced greenery coverage of land surface, which highly affect urban livability. An integrated urban heat island simulation tool is developed by accounting for major heat sources and heat sinks in selected area of interest, and their interactions with the surrounding environment.
Global Warming and the Sudan: Variation of Air Temperature over Sudan and So...Sryahwa Publications
Annual mean air temperature for Sudan and South Sudan for the periods 1961- 1990 and 1981-2010 was obtained as normal data for 19 stations for each period from Sudan Meteorological Authority and analyzed for variation with the course of time and for correlations between mean, maximum and minimum air temperature on the one hand and latitudes, longitudes and elevations on the other hand.
This is the 7th lesson the course - Climate Change & Global Environment taught at the Faculty of Social Sciences and Humanities of the Rajarata University of Sri Lanka
Meridional brightness temperatures were measured on the surface of Titan during the 2004–2014 portion of the
Cassini mission by the Composite Infrared Spectrometer. Temperatures mapped from pole to pole during five twoyear
periods show a marked seasonal dependence. The surface temperature near the south pole over this time
decreased by 2 K from 91.7±0.3 to 89.7±0.5 K while at the north pole the temperature increased by 1 K from
90.7±0.5 to 91.5±0.2 K. The latitude of maximum temperature moved from 19 S to 16 N, tracking the subsolar
latitude. As the latitude changed, the maximum temperature remained constant at 93.65±0.15 K. In 2010
our temperatures repeated the north–south symmetry seen by Voyager one Titan year earlier in 1980. Early in the
mission, temperatures at all latitudes had agreed with GCM predictions, but by 2014 temperatures in the north were
lower than modeled by 1 K. The temperature rise in the north may be delayed by cooling of sea surfaces and moist
ground brought on by seasonal methane precipitation and evaporation.
Modification and Climate Change Analysis of surrounding Environment using Rem...iosrjce
This review is presented in three parts. The first part explains such terms as climate, climate change,
climate change adaptation, remote sensing (RS) and geographical information systems (GIS). The second part
highlights some areas where RS and GIS are applicable in climate change analysis and adaptation. Issues
considered are snow/glacier monitoring, land cover monitoring, carbon trace/accounting, atmospheric
dynamics, terrestrial temperature monitoring, biodiversity conservation, ocean and coast monitoring, erosion
monitoring and control, agriculture, flood monitoring, health and disease, drought and desertification. The
third part concludes from all illustrated instances that climate change problems will be less understood and
managed without the application of RS and GIS. While humanity is still being plagued by climate change effects,
RS and GIS play a crucial role in its management for continued human survival. Key words: Climate, Climate
Change, Climate Change Adaptation, Geographical Information System and Remote Sensing.
4 Review on Different Evapotranspiration Empirical EquationsINFOGAIN PUBLICATION
For optimal design and management of hydrologic balance and scheduling irrigation models, the need to measure Evapotranspiration is of great importance. It helps in predicting when and how much water is required for any particular irrigation scheme. Reference Evapotranspiration is a standard nomenclature defined by FAO to provide a reference frame although it is not a full proof equation. Several scientists have developed multiple equations based of three primary directions viz. temperature based methods, radiation based methods and mass – transfer methods. Here in this paper, we have carried out a review on most of the popular equations and the objective is to elucidate the advantages and drawbacks each one of them register when put into use. The reference equation for standardization considered here is FAO 56 Penman Montheith equation. Thirty other equations from the three schools have been analysed here. Statistical Regression Analysis methods and coefficient of determination (R2), Root Mean Square Error (RMSE) and index of agreement (d) are the analytical parameters those are to be used while estimating their acceptance in evaluating the throughputs
From our climate panel in Grand Junction on August 4:
Our Forest, Our Water, Our Land: Local Impacts on Climate Change. Sponsored by Conservation Colorado, Mesa County Library, Math & Science Center
Global warming &climate changesGlobal temperature measurements remote from human habitation and activity show no evidence of a warming during the last century. Such sites include “proxy” measurements such as tree rings, marine sediments and ice cores, weather balloons and satellite measurements in the lower atmosphere, and many surface sites where human influence is minimal.
To aid in understanding many complex interactions, scientists often build mathematical models that represent simple climate systems. This module highlights the fundamentals of climate models.
diurnal temperature range trend over North Carolina and the associated mechan...Sayem Zaman, Ph.D, PE.
This study seeks to investigate the variability and presence of trends in the diurnal surface air temperature range
(DTR) over North Carolina (NC) for the period 1950–2009. The significance trend test and the magnitude of trends were determined using the non-parametric Mann–Kendall test and the Theil–Sen approach, respectively.
Statewide significant trends (p b 0.05) of decreasing DTR were found in all seasons and annually during the analysis period. The highest (lowest) temporal DTR trends of magnitude −0.19 (−0.031) °C/decade were found in summer (winter). Potential mechanisms for the presence/absence of trends in DTR have been highlighted. Historical
data sets of the three main moisture components (precipitation, total cloud cover (TCC), and soil moisture) and
the two major atmospheric circulation modes (North Atlantic Oscillation and Southern Oscillation) were used for
correlation analysis. The DTRs were found to be negatively correlated with the precipitation, TCC, and soil moisture across the state for all the seasons and annual basis. It appears that the moisture components related better to the DTR than to the atmospheric circulation modes.
Kim Cobb's Borneo stalagmite talk - AGU 2015Kim Cobb
This talk presents the latest results from the Borneo stalagmite project that seeks to reconstruct Western tropical Pacific hydrology over the last half million years. We discuss our results in the context of climate forcing, the El Nino-Southern Oscillation, and climate modeling studies.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Gliese 12 b: A Temperate Earth-sized Planet at 12 pc Ideal for Atmospheric Tr...Sérgio Sacani
Recent discoveries of Earth-sized planets transiting nearby M dwarfs have made it possible to characterize the
atmospheres of terrestrial planets via follow-up spectroscopic observations. However, the number of such planets
receiving low insolation is still small, limiting our ability to understand the diversity of the atmospheric
composition and climates of temperate terrestrial planets. We report the discovery of an Earth-sized planet
transiting the nearby (12 pc) inactive M3.0 dwarf Gliese 12 (TOI-6251) with an orbital period (Porb) of 12.76 days.
The planet, Gliese 12 b, was initially identified as a candidate with an ambiguous Porb from TESS data. We
confirmed the transit signal and Porb using ground-based photometry with MuSCAT2 and MuSCAT3, and
validated the planetary nature of the signal using high-resolution images from Gemini/NIRI and Keck/NIRC2 as
well as radial velocity (RV) measurements from the InfraRed Doppler instrument on the Subaru 8.2 m telescope
and from CARMENES on the CAHA 3.5 m telescope. X-ray observations with XMM-Newton showed the host
star is inactive, with an X-ray-to-bolometric luminosity ratio of log 5.7 L L X bol » - . Joint analysis of the light
curves and RV measurements revealed that Gliese 12 b has a radius of 0.96 ± 0.05 R⊕,a3σ mass upper limit of
3.9 M⊕, and an equilibrium temperature of 315 ± 6 K assuming zero albedo. The transmission spectroscopy metric
(TSM) value of Gliese 12 b is close to the TSM values of the TRAPPIST-1 planets, adding Gliese 12 b to the small
list of potentially terrestrial, temperate planets amenable to atmospheric characterization with JWST.
Gliese 12 b, a temperate Earth-sized planet at 12 parsecs discovered with TES...Sérgio Sacani
We report on the discovery of Gliese 12 b, the nearest transiting temperate, Earth-sized planet found to date. Gliese 12 is a
bright (V = 12.6 mag, K = 7.8 mag) metal-poor M4V star only 12.162 ± 0.005 pc away from the Solar system with one of the
lowest stellar activity levels known for M-dwarfs. A planet candidate was detected by TESS based on only 3 transits in sectors
42, 43, and 57, with an ambiguity in the orbital period due to observational gaps. We performed follow-up transit observations
with CHEOPS and ground-based photometry with MINERVA-Australis, SPECULOOS, and Purple Mountain Observatory,
as well as further TESS observations in sector 70. We statistically validate Gliese 12 b as a planet with an orbital period of
12.76144 ± 0.00006 d and a radius of 1.0 ± 0.1 R⊕, resulting in an equilibrium temperature of ∼315 K. Gliese 12 b has excellent
future prospects for precise mass measurement, which may inform how planetary internal structure is affected by the stellar
compositional environment. Gliese 12 b also represents one of the best targets to study whether Earth-like planets orbiting cool
stars can retain their atmospheres, a crucial step to advance our understanding of habitability on Earth and across the galaxy.
The importance of continents, oceans and plate tectonics for the evolution of...Sérgio Sacani
Within the uncertainties of involved astronomical and biological parameters, the Drake Equation
typically predicts that there should be many exoplanets in our galaxy hosting active, communicative
civilizations (ACCs). These optimistic calculations are however not supported by evidence, which is
often referred to as the Fermi Paradox. Here, we elaborate on this long-standing enigma by showing
the importance of planetary tectonic style for biological evolution. We summarize growing evidence
that a prolonged transition from Mesoproterozoic active single lid tectonics (1.6 to 1.0 Ga) to modern
plate tectonics occurred in the Neoproterozoic Era (1.0 to 0.541 Ga), which dramatically accelerated
emergence and evolution of complex species. We further suggest that both continents and oceans
are required for ACCs because early evolution of simple life must happen in water but late evolution
of advanced life capable of creating technology must happen on land. We resolve the Fermi Paradox
(1) by adding two additional terms to the Drake Equation: foc
(the fraction of habitable exoplanets
with significant continents and oceans) and fpt
(the fraction of habitable exoplanets with significant
continents and oceans that have had plate tectonics operating for at least 0.5 Ga); and (2) by
demonstrating that the product of foc
and fpt
is very small (< 0.00003–0.002). We propose that the lack
of evidence for ACCs reflects the scarcity of long-lived plate tectonics and/or continents and oceans on
exoplanets with primitive life.
A Giant Impact Origin for the First Subduction on EarthSérgio Sacani
Hadean zircons provide a potential record of Earth's earliest subduction 4.3 billion years ago. Itremains enigmatic how subduction could be initiated so soon after the presumably Moon‐forming giant impact(MGI). Earlier studies found an increase in Earth's core‐mantle boundary (CMB) temperature due to theaccumulation of the impactor's core, and our recent work shows Earth's lower mantle remains largely solid, withsome of the impactor's mantle potentially surviving as the large low‐shear velocity provinces (LLSVPs). Here,we show that a hot post‐impact CMB drives the initiation of strong mantle plumes that can induce subductioninitiation ∼200 Myr after the MGI. 2D and 3D thermomechanical computations show that a high CMBtemperature is the primary factor triggering early subduction, with enrichment of heat‐producing elements inLLSVPs as another potential factor. The models link the earliest subduction to the MGI with implications forunderstanding the diverse tectonic regimes of rocky planets.
Climate extremes likely to drive land mammal extinction during next supercont...Sérgio Sacani
Mammals have dominated Earth for approximately 55 Myr thanks to their
adaptations and resilience to warming and cooling during the Cenozoic. All
life will eventually perish in a runaway greenhouse once absorbed solar
radiation exceeds the emission of thermal radiation in several billions of
years. However, conditions rendering the Earth naturally inhospitable to
mammals may develop sooner because of long-term processes linked to
plate tectonics (short-term perturbations are not considered here). In
~250 Myr, all continents will converge to form Earth’s next supercontinent,
Pangea Ultima. A natural consequence of the creation and decay of Pangea
Ultima will be extremes in pCO2 due to changes in volcanic rifting and
outgassing. Here we show that increased pCO2, solar energy (F⨀;
approximately +2.5% W m−2 greater than today) and continentality (larger
range in temperatures away from the ocean) lead to increasing warming
hostile to mammalian life. We assess their impact on mammalian
physiological limits (dry bulb, wet bulb and Humidex heat stress indicators)
as well as a planetary habitability index. Given mammals’ continued survival,
predicted background pCO2 levels of 410–816 ppm combined with increased
F⨀ will probably lead to a climate tipping point and their mass extinction.
The results also highlight how global landmass configuration, pCO2 and F⨀
play a critical role in planetary habitability.
Constraints on Neutrino Natal Kicks from Black-Hole Binary VFTS 243Sérgio Sacani
The recently reported observation of VFTS 243 is the first example of a massive black-hole binary
system with negligible binary interaction following black-hole formation. The black-hole mass (≈10M⊙)
and near-circular orbit (e ≈ 0.02) of VFTS 243 suggest that the progenitor star experienced complete
collapse, with energy-momentum being lost predominantly through neutrinos. VFTS 243 enables us to
constrain the natal kick and neutrino-emission asymmetry during black-hole formation. At 68% confidence
level, the natal kick velocity (mass decrement) is ≲10 km=s (≲1.0M⊙), with a full probability distribution
that peaks when ≈0.3M⊙ were ejected, presumably in neutrinos, and the black hole experienced a natal
kick of 4 km=s. The neutrino-emission asymmetry is ≲4%, with best fit values of ∼0–0.2%. Such a small
neutrino natal kick accompanying black-hole formation is in agreement with theoretical predictions.
Detectability of Solar Panels as a TechnosignatureSérgio Sacani
In this work, we assess the potential detectability of solar panels made of silicon on an Earth-like
exoplanet as a potential technosignature. Silicon-based photovoltaic cells have high reflectance in the
UV-VIS and in the near-IR, within the wavelength range of a space-based flagship mission concept
like the Habitable Worlds Observatory (HWO). Assuming that only solar energy is used to provide
the 2022 human energy needs with a land cover of ∼ 2.4%, and projecting the future energy demand
assuming various growth-rate scenarios, we assess the detectability with an 8 m HWO-like telescope.
Assuming the most favorable viewing orientation, and focusing on the strong absorption edge in the
ultraviolet-to-visible (0.34 − 0.52 µm), we find that several 100s of hours of observation time is needed
to reach a SNR of 5 for an Earth-like planet around a Sun-like star at 10pc, even with a solar panel
coverage of ∼ 23% land coverage of a future Earth. We discuss the necessity of concepts like Kardeshev
Type I/II civilizations and Dyson spheres, which would aim to harness vast amounts of energy. Even
with much larger populations than today, the total energy use of human civilization would be orders of
magnitude below the threshold for causing direct thermal heating or reaching the scale of a Kardashev
Type I civilization. Any extraterrrestrial civilization that likewise achieves sustainable population
levels may also find a limit on its need to expand, which suggests that a galaxy-spanning civilization
as imagined in the Fermi paradox may not exist.
Jet reorientation in central galaxies of clusters and groups: insights from V...Sérgio Sacani
Recent observations of galaxy clusters and groups with misalignments between their central AGN jets
and X-ray cavities, or with multiple misaligned cavities, have raised concerns about the jet – bubble
connection in cooling cores, and the processes responsible for jet realignment. To investigate the
frequency and causes of such misalignments, we construct a sample of 16 cool core galaxy clusters and
groups. Using VLBA radio data we measure the parsec-scale position angle of the jets, and compare
it with the position angle of the X-ray cavities detected in Chandra data. Using the overall sample
and selected subsets, we consistently find that there is a 30% – 38% chance to find a misalignment
larger than ∆Ψ = 45◦ when observing a cluster/group with a detected jet and at least one cavity. We
determine that projection may account for an apparently large ∆Ψ only in a fraction of objects (∼35%),
and given that gas dynamical disturbances (as sloshing) are found in both aligned and misaligned
systems, we exclude environmental perturbation as the main driver of cavity – jet misalignment.
Moreover, we find that large misalignments (up to ∼ 90◦
) are favored over smaller ones (45◦ ≤ ∆Ψ ≤
70◦
), and that the change in jet direction can occur on timescales between one and a few tens of Myr.
We conclude that misalignments are more likely related to actual reorientation of the jet axis, and we
discuss several engine-based mechanisms that may cause these dramatic changes.
The solar dynamo begins near the surfaceSérgio Sacani
The magnetic dynamo cycle of the Sun features a distinct pattern: a propagating
region of sunspot emergence appears around 30° latitude and vanishes near the
equator every 11 years (ref. 1). Moreover, longitudinal flows called torsional oscillations
closely shadow sunspot migration, undoubtedly sharing a common cause2. Contrary
to theories suggesting deep origins of these phenomena, helioseismology pinpoints
low-latitude torsional oscillations to the outer 5–10% of the Sun, the near-surface
shear layer3,4. Within this zone, inwardly increasing differential rotation coupled with
a poloidal magnetic field strongly implicates the magneto-rotational instability5,6,
prominent in accretion-disk theory and observed in laboratory experiments7.
Together, these two facts prompt the general question: whether the solar dynamo is
possibly a near-surface instability. Here we report strong affirmative evidence in stark
contrast to traditional models8 focusing on the deeper tachocline. Simple analytic
estimates show that the near-surface magneto-rotational instability better explains
the spatiotemporal scales of the torsional oscillations and inferred subsurface
magnetic field amplitudes9. State-of-the-art numerical simulations corroborate these
estimates and reproduce hemispherical magnetic current helicity laws10. The dynamo
resulting from a well-understood near-surface phenomenon improves prospects
for accurate predictions of full magnetic cycles and space weather, affecting the
electromagnetic infrastructure of Earth.
Extensive Pollution of Uranus and Neptune’s Atmospheres by Upsweep of Icy Mat...Sérgio Sacani
In the Nice model of solar system formation, Uranus and Neptune undergo an orbital upheaval,
sweeping through a planetesimal disk. The region of the disk from which material is accreted by
the ice giants during this phase of their evolution has not previously been identified. We perform
direct N-body orbital simulations of the four giant planets to determine the amount and origin of solid
accretion during this orbital upheaval. We find that the ice giants undergo an extreme bombardment
event, with collision rates as much as ∼3 per hour assuming km-sized planetesimals, increasing the
total planet mass by up to ∼0.35%. In all cases, the initially outermost ice giant experiences the
largest total enhancement. We determine that for some plausible planetesimal properties, the resulting
atmospheric enrichment could potentially produce sufficient latent heat to alter the planetary cooling
timescale according to existing models. Our findings suggest that substantial accretion during this
phase of planetary evolution may have been sufficient to impact the atmospheric composition and
thermal evolution of the ice giants, motivating future work on the fate of deposited solid material.
Exomoons & Exorings with the Habitable Worlds Observatory I: On the Detection...Sérgio Sacani
The highest priority recommendation of the Astro2020 Decadal Survey for space-based astronomy
was the construction of an observatory capable of characterizing habitable worlds. In this paper series
we explore the detectability of and interference from exomoons and exorings serendipitously observed
with the proposed Habitable Worlds Observatory (HWO) as it seeks to characterize exoplanets, starting
in this manuscript with Earth-Moon analog mutual events. Unlike transits, which only occur in systems
viewed near edge-on, shadow (i.e., solar eclipse) and lunar eclipse mutual events occur in almost every
star-planet-moon system. The cadence of these events can vary widely from ∼yearly to multiple events
per day, as was the case in our younger Earth-Moon system. Leveraging previous space-based (EPOXI)
lightcurves of a Moon transit and performance predictions from the LUVOIR-B concept, we derive
the detectability of Moon analogs with HWO. We determine that Earth-Moon analogs are detectable
with observation of ∼2-20 mutual events for systems within 10 pc, and larger moons should remain
detectable out to 20 pc. We explore the extent to which exomoon mutual events can mimic planet
features and weather. We find that HWO wavelength coverage in the near-IR, specifically in the 1.4 µm
water band where large moons can outshine their host planet, will aid in differentiating exomoon signals
from exoplanet variability. Finally, we predict that exomoons formed through collision processes akin
to our Moon are more likely to be detected in younger systems, where shorter orbital periods and
favorable geometry enhance the probability and frequency of mutual events.
Emergent ribozyme behaviors in oxychlorine brines indicate a unique niche for...Sérgio Sacani
Mars is a particularly attractive candidate among known astronomical objects
to potentially host life. Results from space exploration missions have provided
insights into Martian geochemistry that indicate oxychlorine species, particularly perchlorate, are ubiquitous features of the Martian geochemical landscape. Perchlorate presents potential obstacles for known forms of life due to
its toxicity. However, it can also provide potential benefits, such as producing
brines by deliquescence, like those thought to exist on present-day Mars. Here
we show perchlorate brines support folding and catalysis of functional RNAs,
while inactivating representative protein enzymes. Additionally, we show
perchlorate and other oxychlorine species enable ribozyme functions,
including homeostasis-like regulatory behavior and ribozyme-catalyzed
chlorination of organic molecules. We suggest nucleic acids are uniquely wellsuited to hypersaline Martian environments. Furthermore, Martian near- or
subsurface oxychlorine brines, and brines found in potential lifeforms, could
provide a unique niche for biomolecular evolution.
Continuum emission from within the plunging region of black hole discsSérgio Sacani
The thermal continuum emission observed from accreting black holes across X-ray bands has the potential to be leveraged as a
powerful probe of the mass and spin of the central black hole. The vast majority of existing ‘continuum fitting’ models neglect
emission sourced at and within the innermost stable circular orbit (ISCO) of the black hole. Numerical simulations, however,
find non-zero emission sourced from these regions. In this work, we extend existing techniques by including the emission
sourced from within the plunging region, utilizing new analytical models that reproduce the properties of numerical accretion
simulations. We show that in general the neglected intra-ISCO emission produces a hot-and-small quasi-blackbody component,
but can also produce a weak power-law tail for more extreme parameter regions. A similar hot-and-small blackbody component
has been added in by hand in an ad hoc manner to previous analyses of X-ray binary spectra. We show that the X-ray spectrum
of MAXI J1820+070 in a soft-state outburst is extremely well described by a full Kerr black hole disc, while conventional
models that neglect intra-ISCO emission are unable to reproduce the data. We believe this represents the first robust detection of
intra-ISCO emission in the literature, and allows additional constraints to be placed on the MAXI J1820 + 070 black hole spin
which must be low a• < 0.5 to allow a detectable intra-ISCO region. Emission from within the ISCO is the dominant emission
component in the MAXI J1820 + 070 spectrum between 6 and 10 keV, highlighting the necessity of including this region. Our
continuum fitting model is made publicly available.
WASP-69b’s Escaping Envelope Is Confined to a Tail Extending at Least 7 RpSérgio Sacani
Studying the escaping atmospheres of highly irradiated exoplanets is critical for understanding the physical
mechanisms that shape the demographics of close-in planets. A number of planetary outflows have been observed
as excess H/He absorption during/after transit. Such an outflow has been observed for WASP-69b by multiple
groups that disagree on the geometry and velocity structure of the outflow. Here, we report the detection of this
planet’s outflow using Keck/NIRSPEC for the first time. We observed the outflow 1.28 hr after egress until the
target set, demonstrating the outflow extends at least 5.8 × 105 km or 7.5 Rp This detection is significantly longer
than previous observations, which report an outflow extending ∼2.2 planet radii just 1 yr prior. The outflow is
blueshifted by −23 km s−1 in the planetary rest frame. We estimate a current mass-loss rate of 1 M⊕ Gyr−1
. Our
observations are most consistent with an outflow that is strongly sculpted by ram pressure from the stellar wind.
However, potential variability in the outflow could be due to time-varying interactions with the stellar wind or
differences in instrumental precision.
X-rays from a Central “Exhaust Vent” of the Galactic Center ChimneySérgio Sacani
Using deep archival observations from the Chandra X-ray Observatory, we present an analysis of
linear X-ray-emitting features located within the southern portion of the Galactic center chimney,
and oriented orthogonal to the Galactic plane, centered at coordinates l = 0.08◦
, b = −1.42◦
. The
surface brightness and hardness ratio patterns are suggestive of a cylindrical morphology which may
have been produced by a plasma outflow channel extending from the Galactic center. Our fits of the
feature’s spectra favor a complex two-component model consisting of thermal and recombining plasma
components, possibly a sign of shock compression or heating of the interstellar medium by outflowing
material. Assuming a recombining plasma scenario, we further estimate the cooling timescale of this
plasma to be on the order of a few hundred to thousands of years, leading us to speculate that a
sequence of accretion events onto the Galactic Black Hole may be a plausible quasi-continuous energy
source to sustain the observed morphology
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Leading Change strategies and insights for effective change management pdf 1.pdf
Human and natural_influences_on_the_changing_thermal_structure_of_the_atmosphere
1. Human and natural influences on the changing thermal
structure of the atmosphere
Benjamin D. Santera,1
, Jeffrey F. Paintera
, Céline Bonfilsa
, Carl A. Mearsb
, Susan Solomonc
, Tom M. L. Wigleyd,e
,
Peter J. Glecklera
, Gavin A. Schmidtf
, Charles Doutriauxa
, Nathan P. Gillettg
, Karl E. Taylora
, Peter W. Thorneh
,
and Frank J. Wentzb
a
Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA 94550; b
Remote Sensing Systems, Santa
Rosa, CA 95401; c
Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139; d
National Center for Atmospheric
Research, Boulder, CO 80307; e
School of Earth and Environmental Sciences, University of Adelaide, Adelaide, SA 5005, Australia; f
National Aeronautics and
Space Administration/Goddard Institute for Space Studies, New York, NY 10025; g
Canadian Centre for Climate Modelling and Analysis, Environment Canada,
Victoria, BC, Canada V8W 2Y2; and h
Nansen Environmental and Remote Sensing Center, N-5006 Bergen, Norway
Edited by John M. Wallace, University of Washington, Seattle, WA, and approved August 8, 2013 (received for review March 20, 2013)
Since the late 1970s, satellite-based instruments have monitored
global changes in atmospheric temperature. These measurements
reveal multidecadal tropospheric warming and stratospheric cooling,
punctuated by short-term volcanic signals of reverse sign. Similar
long- and short-term temperature signals occur in model simula-
tions driven by human-caused changes in atmospheric composition
and natural variations in volcanic aerosols. Most previous compar-
isons of modeled and observed atmospheric temperature changes
have used results from individual models and individual observa-
tional records. In contrast, we rely on a large multimodel archive and
multiple observational datasets. We show that a human-caused
latitude/altitude pattern of atmospheric temperature change can be
identified with high statistical confidence in satellite data. Results are
robust to current uncertainties in models and observations. Virtually
all previous research in this area has attempted to discriminate an
anthropogenic signal from internal variability. Here, we present
evidence that a human-caused signal can also be identified relative
to the larger “total” natural variability arising from sources internal
to the climate system, solar irradiance changes, and volcanic forcing.
Consistent signal identification occurs because both internal and
total natural variability (as simulated by state-of-the-art models)
cannot produce sustained global-scale tropospheric warming and
stratospheric cooling. Our results provide clear evidence for a discern-
ible human influence on the thermal structure of the atmosphere.
climate change detection | climate modeling
Global changes in the physical climate system are driven by
both internal variability and external influences (1, 2). In-
ternal variability is generated by complex interactions of the
coupled atmosphere–ocean system, such as the well-known El
Niño/Southern Oscillation. External influences include human-
caused changes in well-mixed greenhouse gases, stratospheric
ozone, and other radiative forcing agents, as well as natural fluc-
tuations in solar irradiance and volcanic aerosols. Each of these
external influences has a unique “fingerprint” in the detailed lat-
itude/altitude pattern of atmospheric temperature change (3–8).
The use of such fingerprint information has proved particularly
useful in separating human, solar, and volcanic influences on cli-
mate, and in discriminating between externally forced signals and
internal variability (3–7).
We have two main scientific objectives. The first is to consider
whether a human-caused fingerprint can be identified against the
“total” natural variability ðVTOTÞ arising from the combined
effects of internal oscillatory behavior ðVINTÞ, solar irradiance
changes, and fluctuations in atmospheric loadings of volcanic
aerosols. To date, only one signal detection study (involving
hemispheric-scale surface temperature changes) has relied on
VTOT information (9). All other pattern-based fingerprint studies
have tested against VINT (2, 4–7, 10, 11). When fingerprint inves-
tigations use information from simulations with natural external
forcing, it is typically for the purpose of ascertaining whether
model-predicted solar and volcanic signals are detectable in ob-
servational climate records, and whether the amplitude of the
model signals is consistent with observed estimates of signal
strength (7, 12, 13).
We are addressing a different statistical question here. We
seek to determine whether observed changes in the large-scale
thermal structure of the atmosphere are truly unusual relative
to the best current estimates of the total natural variability of the
climate system. The significance testing framework applied here
is highly conservative. Our VTOT estimates incorporate variability
information from 850 AD to 2005, and sample substantially
larger naturally forced changes in volcanic aerosol loadings and
solar irradiance than have been observed over the satellite era.
Our second objective is to examine the sensitivity of finger-
print results to current uncertainties in models and observations.
With one exception (11), previous fingerprint studies of changes
in the vertical structure of atmospheric temperature have used
information from individual models. An additional concern is
that observational uncertainty is rarely considered in such work
(3–7). These limitations have raised questions regarding the re-
liability of fingerprint-based findings of a discernible human in-
fluence on climate (14).
Model and Observational Temperature Data
The model output analyzed here is from phase 5 of the Coupled
Model Intercomparison Project (CMIP-5) (15). We use atmospheric
Significance
Observational satellite data and the model-predicted response
to human influence have a common latitude/altitude pattern of
atmospheric temperature change. The key features of this pat-
tern are global-scale tropospheric warming and stratospheric
cooling over the 34-y satellite temperature record. We show that
current climate models are highly unlikely to produce this dis-
tinctive signal pattern by internal variability alone, or in re-
sponse to naturally forced changes in solar output and volcanic
aerosol loadings. We detect a “human influence” signal in all
cases, even if we test against natural variability estimates with
much larger fluctuations in solar and volcanic influences than
those observed since 1979. These results highlight the very un-
usual nature of observed changes in atmospheric temperature.
Author contributions: B.D.S., C.B., C.A.M., S.S., T.M.L.W., K.E.T., P.W.T., and F.J.W. designed
research; B.D.S., J.F.P., C.B., C.A.M., P.J.G., G.A.S., and C.D. performed research; B.D.S., C.B.,
C.A.M., S.S., T.M.L.W., G.A.S., K.E.T., and P.W.T. analyzed data; B.D.S., C.B., S.S., T.M.L.W., G.A.S.,
N.P.G., and P.W.T. wrote the paper; and C.A.M. and F.J.W. provided key satellite datasets.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Freely available online through the PNAS open access option.
1
To whom correspondence should be addressed. E-mail: santer1@llnl.gov.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1305332110/-/DCSupplemental.
www.pnas.org/cgi/doi/10.1073/pnas.1305332110 PNAS Early Edition | 1 of 6
EARTH,ATMOSPHERIC,
ANDPLANETARYSCIENCES
2. temperature changes from simulations with estimated historical
changes in these factors: (i) combined human and natural ex-
ternal forcings (ALL); (ii) anthropogenic forcings only (ANT);
(iii) combined solar and volcanic forcing only (NAT); (iv) solar
forcing only (SOL); and (v) volcanic forcing only (VOL). We also
analyze integrations with the following: (vi) estimated changes
in solar and volcanic forcing over the past 1,000 y (P1000);
(vii) no changes in external influences (CTL); and (viii) 21st cen-
tury changes in greenhouse gases and anthropogenic aerosols (16)
specified according to Representative Concentration Pathway
8.5 (RCP8.5).
We compare simulation output with observed atmospheric
temperature changes inferred from satellite-based Microwave
Sounding Units (MSUs). Our focus is on zonally averaged
temperature changes for three broad layers of the atmosphere:
the lower stratosphere (TLS), the mid- to upper troposphere
(TMT), and the lower troposphere (TLT) (1). We use observa-
tional MSU information from two different groups: Remote
Sensing Systems (RSS) (17) and the University of Alabama at
Huntsville (UAH) (18). An important aspect of our fingerprint
study is its use of additional estimates of observational un-
certainty provided by the RSS group (17) (SI Appendix).
Two processing choices facilitate the comparison of models
and observations. First, we calculate synthetic MSU temper-
atures from CMIP-5 simulations, so that modeled and observed
layer-averaged temperatures are vertically weighted in a similar
way (10). Second, we splice together temperature information
from the ALL and RCP8.5 simulations. The latter are initiated
from the end of the ALL simulations, which was generally in
December 2005 (SI Appendix). Splicing makes it possible to
compare modeled and observed temperature changes over the
full observed satellite record. We refer to these spliced simu-
lations as “ALL+8.5.” (The ANT, NAT, VOL, and SOL inte-
grations also end in December 2005. Unlike the ALL simulation,
they cannot be spliced with RCP8.5 results without introducing
a discontinuity in forcing.)
Global-Mean Temperature Changes
Fig. 1 shows the multimodel average changes in global-mean
atmospheric temperature in the NAT and ALL+8.5 simulations.
In both types of numerical experiment, the stratosphere warms
and the troposphere cools after major volcanic eruptions (1, 4–8,
19, 20). The abrupt TLS warming signals (Fig. 1A) are due to the
absorption of incoming solar radiation and outgoing long-wave
radiation by volcanic aerosols injected into the stratosphere (21).
Stratospheric volcanic aerosols also reduce the clear-sky solar
radiation received at Earth’s surface, leading to surface and
tropospheric cooling. Because of the large thermal inertia of the
oceanic mixed layer, the recovery of tropospheric temperature
from volcanically induced cooling can take up to a decade (Fig. 1
B and C). The removal of volcanic aerosols and the recovery of
lower stratospheric temperature is more rapid (∼2 y).
The ALL+8.5 simulations exhibit sustained cooling of the
lower stratosphere and warming of the troposphere over the past
60 y (Fig. 1). The decrease in TLS is primarily a response to
human-caused stratospheric ozone depletion, with a smaller
contribution from anthropogenic changes in other greenhouse
gases (GHGs) (19, 22, 23). Tropospheric warming is mainly driven
by anthropogenic GHG increases (1, 2, 8, 23, 24). In contrast, the
NAT runs do not produce large, multidecadal temperature changes
(Fig. 1 and SI Appendix, Figs. S1 and S2).
After removing the climatological seasonal cycle, lower strato-
spheric temperature anomalies exhibit a large (post-1970) residual
seasonal cycle in the ALL+8.5 simulation, but not in the NAT
integration (Fig. 1A). This residual seasonality arises because of
the pronounced impact of stratospheric ozone depletion on the
seasonal cycle of TLS, particularly at high latitudes in the Southern
Hemisphere (25, 26) (SI Appendix, Fig. S3).
Latitude/Altitude Patterns of Temperature Change
Fig. 2 shows the vertical structure of zonal-mean atmospheric
temperature trends in the observations and the ALL+8.5, ANT,
NAT, VOL, and SOL simulations. Because we perform our
subsequent fingerprint analysis in “MSU space,” with only three
atmospheric layers (TLS, TMT, and TLT), we use the same
MSU space here for visual display of temperature trends. This
provides a vertically smoothed picture of temperature changes
over the satellite era, while still preserving the principal large-
scale features of externally forced signals. [The contouring al-
gorithm used to generate Fig. 2 interpolates temperature in-
formation between vertical layers, and between 58 latitude bands
(see legend of Fig. 2).]
The ALL+8.5 and ANT multimodel averages (Fig. 2 A and D)
and the observations (Fig. 2 H and I) are characterized by similar
patterns of large-scale tropospheric warming and lower strato-
spheric cooling. In the ALL+8.5 simulations, the most pronounced
intermodel differences in temperature trends are in the vicinity of
the Antarctic ozone hole (Fig. 2B and SI Appendix, Fig. S4), where
internal variability is large (10), and there are appreciable inter-
model differences in historical ozone forcing (27).
If we use the ratio R1 as a measure of the size of the multimodel
average ALL+8.5 trend relative to the intermodel SD of ALL+8.5
temperature trends, this metric exceeds two over substantial
portions of the troposphere and lower stratosphere (Fig. 2C). The
R1 results demonstrate that the ALL+8.5 pattern of tropospheric
warming and stratospheric cooling is robust to current uncer-
tainties in external forcings and model temperature responses.
-2
-1
0
1
2
3
4
TLSanomaly(
o
C)
A
Lower stratosphere
Atmospheric Temperature Changes in CMIP-5 Simulations
-1
-0.5
0
0.5
1
1.5
2
TMTanomaly(
o
C)
B Mid- to upper troposphere
Model average (ALL+8.5) +/- 2 sigma
Model average (NAT) +/- 2 sigma
Model average (NAT)
Model average (ALL+8.5)
1860 1880 1900 1920 1940 1960 1980 2000
-1
-0.5
0
0.5
1
1.5
2
TLTanomaly(
o
C)
C Lower troposphere
Fig. 1. Time series of simulated monthly mean near-global anomalies in the
temperature of the lower stratosphere (TLS), the mid- to upper troposphere
(TMT), and the lower troposphere (TLT) (A–C). Model results are from spliced
historical/RCP8.5 simulations with combined anthropogenic and natural ex-
ternal forcing (ALL+8.5) and from simulations with natural external forcing
only (NAT). The bold lines denote the ALL+8.5 and NAT multimodel aver-
ages, calculated with 20 and 16 CMIP-5 models (respectively). Temperatures
are averaged over 82.5°N–82.5°S for TLS and TMT, and over 82.5°N–70°S for
TLT. Anomalies are defined with respect to climatological monthly means
over 1861–1870. The shaded envelopes are the multimodel averages ± 2 × sðtÞ,
where sðtÞ is the “between model” SD of the 20 (ALL+8.5) and 16 (NAT) en-
semble-mean anomaly time series. To aid visual discrimination of the over-
lapping ALL+8.5 and NAT envelopes, the boundaries of the ALL+8.5 envelope
are indicated by dotted orange lines.
2 of 6 | www.pnas.org/cgi/doi/10.1073/pnas.1305332110 Santer et al.
3. Anthropogenic forcing makes the largest contribution to the
ALL+8.5 temperature-change pattern (Fig. 2 A and D–G). The
NAT contribution is relatively small, but augments the anthro-
pogenic signal. Over 1979–2005, the NAT contribution is domi-
nated by volcanic effects, which generate a slight warming trend in
the troposphere and a small cooling trend in the stratosphere (SI
Appendix, Fig. S5). Because there is little or no trend in solar ir-
radiance over the satellite era, the simulated solar signal is weak.
It is difficult to make more rigorous quantitative comparisons
of the temperature changes in the ALL+8.5, ANT, NAT, SOL,
and VOL simulations. This difficulty arises because of (i) “be-
tween experiment” differences in the number of models and real-
izations available for estimating multimodel averages (SI Appendix);
and (ii) “between model” differences in external forcings (27) and
climate sensitivity (28). The information provided in Fig. 2, how-
ever, represents our current best multimodel estimate of the
patterns and relative sizes of anthropogenically and naturally
forced atmospheric temperature changes over the satellite era.
Leading Signal and Noise Patterns
We use a standard fingerprint method (29) to compare model-
predicted vertical patterns of zonal-mean atmospheric temper-
ature change with satellite observations (SI Appendix). The
searched-for fingerprint is the climate-change signal in response
to a set of external forcings. Here, the fingerprint is defined as the
first empirical orthogonal function (EOF) of Sðx; h; tÞ, the multi-
model average of zonal-mean synthetic MSU temperature changes
in the ANT or ALL+8.5 simulations. [The double overbar in
Sðx; h; tÞ indicates two averaging steps: an average over ANT or
ALL+8.5 realizations of an individual model (if multiple real-
izations are available) and an average over models.]
Zonal-Mean Atmospheric Temperature Trends in CMIP-5 Models and Observations
-0.3 -0.2 -0.1 0 0.1 0.2 0.3
-0.25 -0.15 -0.05 0.05 0.15 0.25
A ALL+8.5 (model average trend) B ALL+8.5 (trend uncertainty) C ALL+8.5 (ratio R1)
-0.25 -0.15 -0.05 0.05 0.15 0.25
-0.3 -0.2 -0.1 0.0 0.1 0.2 0.3
0.06 0.12 0.18 0.24 0.3 0.36
0.03 0.09 0.15 0.21 0.27 0.33 0.39
-2.5 -1.5 -0.5 0.5 1.5 2.5
-3 -2 -1 0 1 2 3
D ANT NAT VOL
SOL RSS v3.3 UAH v5.4
R1 (dimensionless)
E F
G H I
Fig. 2. Zonal-mean atmospheric temperature trends in CMIP-5 models (A and D–G) and observations (H and I). Trends were calculated after first regridding
model and observational TLS, TMT, and TLT anomaly data to a 58 × 58 latitude/longitude grid, and then computing zonal averages. Results are plotted in “MSU
space,” at the approximate peaks of the TLS, TMT, and TLT global-mean MSU weighting functions (74, 595, and 740 hPa, respectively). Trends in the RSS and
UAH observations and the ALL+8.5 simulations are for the 408 months from January 1979 to December 2012. For the shorter ANT, NAT, VOL, and SOL
simulations, trends are over January 1979 to December 2005. The ALL+8.5, ANT, NAT, VOL, and SOL trends are multimodel averages, computed with 20, 8, 16,
2, and 3 models (respectively). B shows a simple measure of model uncertainty in the ALL+8.5 trends: sðx,hÞ, the intermodel SD of the 20 individual ensemble-
mean trends. The ratio R1 in C is the ALL+8.5 multimodel average trend in A, bðx,hÞ, divided by sðx,hÞ in B.
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4. As in the observations (Fig. 2 H and I), both the ANT and
ALL+8.5 fingerprints show spatially coherent warming of the
troposphere and cooling of the lower stratosphere (Fig. 3 A and B).
The similarity of the ANT and ALL+8.5 fingerprints arises because
model trends in atmospheric temperature over the past 30–60 y
are primarily driven by anthropogenic influences, with only a small
contribution from solar and volcanic forcing (Figs. 1 and 2).
Before presenting the results of our fingerprint analysis, we
first examine the major modes of internal and total natural
variability (Fig. 3 C–K). These are characterized by the leading
EOFs calculated from the CTL, NAT, and P1000 simulations (SI
Appendix). In the first EOF of the CTL simulations, temperature
changes in the tropics and extratropics are negatively correlated
(Fig. 3C). The leading mode in the NAT and P1000 simulations
used to estimate VTOT (Fig. 3 F and I) captures both the strato-
spheric warming and tropospheric cooling in response to large
volcanic eruptions and part of the internal variability manifest in
CTL EOF 1 (Fig. 3C). The natural variability modes in Fig. 3 C–K
lack the pattern of global-scale tropospheric warming and
stratospheric cooling that is evident in the observations (Fig. 2 H
and I) and the ANT and ALL+8.5 fingerprints (Fig. 3 A and B).
Fingerprint Results
We consider next the detectability of the ANT fingerprint. If the
amplitude of the fingerprint pattern Fðx; hÞ is increasing in
Oðx; h; tÞ, the time-varying observations, there will be a positive
trend in cfF; OgðtÞ, the covariance statistic that measures the
spatial similarity between Fðx; hÞ and Oðx; h; tÞ (SI Appendix). [The
indices x, h, and t are (respectively) over the total number of lat-
itude bands, atmospheric layers, and time (in years).] These “sig-
nal trends,” bðLÞ, are a function of the analysis period L, which
spans lengths of 10–34 y (i.e., from 1979–1988 to 1979–2012).
As L increases, the spatial similarity between Oðx; h; tÞ and the
ANT fingerprint decreases initially due to the stratospheric
warming and tropospheric cooling caused by the 1991 Pinatubo
eruption (Fig. 4A). This reduces the magnitude of bðLÞ values.
Leading Signal and Natural Variability Modes in CMIP-5 Models
EOF loading
-1.2 -0.8 -0.4 0 0.4 0.8 1.2
-1.0 -0.6 -0.2 0.2 0.6 1.0
A ANT signal EOF1 (90.14%)
B ALL+8.5 signal EOF1 (87.11%)
CTL noise EOF1 (31.85%) CTL noise EOF2 (23.21%) CTL noise EOF3 (17.31%)
NAT noise EOF1 (34.72%) NAT noise EOF2 (18.38%) NAT noise EOF3 (15.3%)
P1000 noise EOF1 (48.84%) P1000 noise EOF2 (15.73%) P1000 noise EOF3 (12.07%)
D E
F G H
I J K
C
Fig. 3. Leading signal and natural variability modes for the vertical structure of atmospheric temperature change in CMIP-5 simulations. All signal and
natural variability modes were calculated after first transforming annual-mean synthetic TLS, TMT, and TLT data to a common 58 × 58 latitude/longitude grid,
and then computing zonal averages. The leading signal modes are the first EOFs of the multimodel atmospheric temperature changes in the ANT and ALL+8.5
simulations (A and B, respectively). Multimodel averages were calculated over 1861–2012 for the ALL+8.5 case, and over 1861–2005 for the shorter ANT
simulations, using results from 20 ALL+8.5 models and 8 ANT models. The leading natural variability modes are EOFs 1, 2, and 3 of the 20 concatenated
preindustrial control runs (CTL; C–E), the 16 concatenated simulations with estimates of historical changes in solar and volcanic forcing over 1850–2005 (NAT;
F–H), and the 6 concatenated integrations with natural external forcing over 850–1700 (P1000; I–K). The percentage variance explained by each mode is given
in parentheses. See SI Appendix for further analysis details.
4 of 6 | www.pnas.org/cgi/doi/10.1073/pnas.1305332110 Santer et al.
5. During the recovery phase after Pinatubo, signal trends increase
until L = 20 years. Subsequently, following the large tropospheric
warming caused by the 1997/1998 El Niño, the amplitude of bðLÞ
gradually decreases. This decrease is due to changes in observed
rates of stratospheric cooling and troposphere warming (10, 30).
The crux of the fingerprint identification problem is to assess
whether these signal trends are statistically significant. We use
signal-to-noise (S/N) ratios to make this determination. To es-
timate the denominator of the S/N ratio, we require “null” (no
signal) distributions for trends of length L years. Conventionally,
these distributions are obtained using internal variability in-
formation from many L-year segments of CTL simulations.
Here, we also consider the additional variability arising from
solar and volcanic forcing, which we estimate using both the
NAT integrations and the longer P1000 runs. This gives us three
different sets of natural variability estimates and S/N ratios (Fig.
4 B and C; green, blue, and red curves).
We obtain “no signal” distributions by comparing Fðx; hÞ with
Nðx; h; tÞ, the temperature changes from the concatenated CTL,
NAT, or P1000 integrations. This yields long time series of the
pattern similarity statistic cfF; NgðtÞ, from which the null dis-
tributions can be calculated for varying trend lengths. These
distributions have means close to zero and standard deviations
sðLÞ that decrease by a factor of roughly 5 as L increases from
10 to 34 y (Fig. 4B).
The S/N ratio that we use for assessing the statistical signifi-
cance of signal trends is simply given by bðLÞ=sðLÞ (Fig. 4C). [For
L = 10, therefore, bðLÞ is calculated over 1979–1988, and sðLÞ is
computed from the distribution of nonoverlapping 10-y trends in
cfF; NgðtÞ.] S/N ratios generally increase with longer analysis
periods, primarily because of the decrease in sðLÞ with larger
values of L. With CTL noise, S/N ratios for signal trends com-
puted over 1979–2012 are invariably significant at the 1% level or
better, and range from 8.4 to 10.7, depending on the choice of
observational dataset.
Consider next the S/N results for tests against VTOT. The NAT
simulations provide estimates of how atmospheric temperature
might have evolved in the absence of human intervention, but in
the presence of stochastic temperature changes arising from in-
ternal variability and deterministic changes caused by solar and
volcanic forcing. One possible significance testing strategy is to
restrict our estimate of VTOT to the period of overlap between
the NAT runs and the satellite data sets (1979–2005). This strategy
has two disadvantages: (i) we have only 16 NAT models with
samples of naturally forced temperature change over 1979–2005;
and (ii) each of these samples includes only two major volcanic
eruptions (El Chichón and Pinatubo).
Here, we estimate VTOT over 1861–2005, and thus do not re-
quire that the simulated and observed evolution of volcanic forc-
ing is identical. By using this longer period, we include the effects
of four additional major eruptions in the presatellite era (Krakatau
in 1883, Soufrière/Pelée/Santa Maria in 1902, Novarupta in 1912,
and Agung in 1963) and obtain many more samples of the tem-
perature response to volcanic forcing. This increase in sample size
is advantageous in assessing the likelihood of obtaining the observed
signal trends by total natural variability alone.
As expected, trends computed from the NAT simulations are
generally larger than those obtained from the CTL runs (Fig. 4B).
This holds for all timescales examined here. Despite the increase
in the size of the denominator, S/N ratios remain highly signifi-
cant for signal trends calculated over the full satellite record,
ranging from 3.7 to 4.8 (Fig. 4C). It is unlikely that these values
are spuriously inflated by a systematic underestimate of total
natural variability in the CMIP-5 models analyzed here (10).
Although there are large uncertainties in the solar and vol-
canic forcings used in the six P1000 runs (31), these simulations
provide our best current estimates of the magnitude and patterns
of naturally forced atmospheric temperature change over the
period from 850 to 1849 (SI Appendix, Fig. S6). As in the case of
the NAT simulations, we use P1000 VTOT estimates to determine
whether an anthropogenic fingerprint can be identified relative to
total natural variability levels that are substantially larger than
those actually sampled over the satellite era.
In addition to solar and volcanic forcing, the P1000 simu-
lations include anthropogenic changes in GHGs and land use
(31). To avoid appreciable anthropogenic contamination, VTOT
values were calculated using synthetic MSU temperatures for
850–1700 only. This period contains at least two massive volcanic
eruptions—an unknown eruption in 1259, and Kuwae in 1452.
Each event is estimated to have produced larger stratospheric
sulfate aerosol loadings than those of any eruption during the
NAT simulation period (32). This explains why the P1000 levels
of total natural variability are consistently higher than those
computed with NAT simulations (Fig. 4B). Even with these very
large P1000 VTOT values, we still obtain ubiquitous detection of
an anthropogenic fingerprint in the observations, with S/N ratios
ranging from 2.5 to 3.2 for 34-y trends (Fig. 4C).
Sensitivity Tests
We performed a number of additional sensitivity studies to ex-
plore the robustness of these results. The first involved use of the
1990 1995 2000 2005 2010
Last year of L-year linear trend in signal
0
0.1
0.2
0.3
0.4
0.5
Trendinc{F,O}(t)
RSS 5-95 percentiles
RSS v3.3
UAH v5.5
Signal Trends, Noise Trends, and S/N Ratios
Zonal-mean TLS, TMT, and TLT. ANT fingerprint (1861-2005)
10 15 20 25 30
Length of noise trend (years)
0
0.1
0.2
0.3
0.4
0.5
Trendinc{F,N}(t)
Std. deviation of noise trends
CTL noise
NAT noise
P1000 noise
1990 1995 2000 2005 2010
Last year of L-year linear trend in signal
0
2
4
6
8
10
S/Nratio
S/N ratio
RSS v3.3 (CTL noise)
UAH v5.5 (CTL noise)
RSS v3.3 (NAT noise)
UAH v5.5 (NAT noise)
RSS v3.3 (P1000 noise)
UAH v5.5 (P1000 noise)
1% significance threshold
Signal trends
A
B
C
Fig. 4. Results from the S/N analysis of simulated and observed changes in
zonal-mean TLS, TMT, and TLT. Signal time series provide information on the
similarity between the time-invariant ANT fingerprint pattern (Fig. 3A) and
the time-varying observed patterns of zonal-mean atmospheric temperature
change. Values of bðLÞ, the L-year trends in these signal time series, are
plotted in A. Noise time series indicate the level of similarity between the
ANT fingerprint and the CTL, NAT, and P1000 estimates of variability. B
shows sðLÞ, the SD of the distribution of nonoverlapping L-year trends in the
CTL, NAT, and P1000 noise time series. The S/N ratio between bðLÞ and sðLÞ is
given in C. The thin solid lines in C are the S/N ratios for signal trends
obtained with the RSS 5–95 percentiles. The nominal 1% significance level
assumes a Gaussian distribution of noise trends. The ANT fingerprint was
calculated using the multimodel average zonal-mean changes in atmo-
spheric temperature over 1861–2005 (SI Appendix). Signal and noise trends in
A and B have units of cfF,Og=decade and cfF,Ng=decade, respectively.
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6. ALL+8.5 rather than the ANT fingerprint. Because of the spatial
similarity of these fingerprints, they yield similar S/N ratios (Fig.
4 and SI Appendix, Fig. S7). In a second test, we repeated the
entire fingerprint analysis with zonal-mean changes in TLS and
TLT only. Temperature changes have more favorable S/N
characteristics in the lower stratosphere than in the troposphere
(10), so removal of zonal-mean TMT changes substantially
increases S/N ratios (SI Appendix, Fig. S8). In our third test, ALL
+8.5 and ANT fingerprints were estimated over the satellite era
only (rather than over the full period of these simulations). Use
of a shorter period for fingerprint estimation still preserves the
large-scale features of tropospheric warming and stratospheric
cooling (Fig. 2 A and D), so fingerprint detection is insensitive to
this analysis choice.
One area of concern is that, on average, the ALL+8.5 simu-
lations underestimate the observed lower stratospheric cooling
and overestimate tropospheric warming (compare Fig. 2A with
Fig. 2 H and I). These differences must be due to some combi-
nation of errors in model forcings (27, 33–35), model response
errors (36), residual observational inhomogeneities (17), and an
unusual manifestation of natural internal variability in the obser-
vations (10, 30). Because of the bias in tropospheric warming, most
individual models have S/N ratios that are larger than those
obtained with observations (SI Appendix, Fig. S9).
Conclusions
Our analysis of the latest satellite datasets and model simulations
reveals that a model-predicted anthropogenic fingerprint pattern
is consistently identifiable, with high statistical confidence, in the
changing thermal structure of the atmosphere. Multidecadal
tropospheric warming and lower stratospheric cooling are the
main features of this fingerprint. Tests against NAT and P1000
“total” natural variability ðVTOTÞ demonstrate that observed
temperature changes are not simply a recovery from the El
Chichón and Pinatubo events, and/or a response to variations in
solar irradiance. The significance testing framework used here is
highly conservative—the NAT and P1000 estimates of VTOT in-
clude volcanic eruptions and solar irradiance changes much larger
than those observed over the satellite era. Our results are robust
to current uncertainties in models and observations, and un-
derscore the dominant role human activities have played in recent
climate change.
ACKNOWLEDGMENTS. We acknowledge the World Climate Research Pro-
gramme’s Working Group on Coupled Modelling, which is responsible for
CMIP, and we thank the climate modeling groups (listed in SI Appendix,
Table S1) for producing and making available their model output. For CMIP,
the Department of Energy (DOE) Program for Climate Model Diagnosis and
Intercomparison (PCMDI) provides coordinating support and led develop-
ment of software infrastructure in partnership with the Global Organization
for Earth System Science Portals. Helpful comments and advice were pro-
vided by Jim Boyle (PCMDI), Kerry Emanuel (Massachusetts Institute of Tech-
nology), and Mike MacCracken. At Lawrence Livermore National Laboratory,
work by B.D.S., J.F.P., P.J.G., and K.E.T. was performed under the auspices of
the DOE under Contract DE-AC52-07NA27344; C.B. was supported by the
DOE/Office of Biological and Environmental Research (OBER) Early Career
Research Program Award SCW1295; and C.D. was funded under DOE/OBER
Contract DE-AC52-07NA27344.
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