"Pattern scaling using ClimGen: users needs, changing precipitation variability, and interaction between global/regional responses" presentation by Tim Osborn and Craig Wallace, NCAR, April 2014
Presentation from the Kick-off Meeting "Seasonal to Decadal Forecast towards Climate Services: Joint Kickoff Meetings" for ECOMS, EUPORIAS, NACLIM and SPECS FP7 projects.
1.1 Climate change and impacts on hydrological extremes (P.Willems)Stevie Swenne
Presentation of Patrick Willems (KU Leuven) on 'Climate change and impacts on hydrological extremes' during the conference 'Environmental challenges & Climate change opportunities' organised by Flanders Environment Agency (VMM)
Slides from a presentation about modeling past and future climate as part of the "School of Ice" workshop for educators at Oregon State University on Aug. 2, 2021.
Presentation from the workshop 'Informing and Enabling a Climate Resilient Ireland”' - held 23 March 2012. This event launched 2 EPA Climate Change Research Programme reports:
CCRP9 'Ireland adapts to Climate Change' and CCRP10 'Integrating Climate Change Adaptation into Sectoral Policies in Ireland'
Presentation from the Kick-off Meeting "Seasonal to Decadal Forecast towards Climate Services: Joint Kickoff Meetings" for ECOMS, EUPORIAS, NACLIM and SPECS FP7 projects.
1.1 Climate change and impacts on hydrological extremes (P.Willems)Stevie Swenne
Presentation of Patrick Willems (KU Leuven) on 'Climate change and impacts on hydrological extremes' during the conference 'Environmental challenges & Climate change opportunities' organised by Flanders Environment Agency (VMM)
Slides from a presentation about modeling past and future climate as part of the "School of Ice" workshop for educators at Oregon State University on Aug. 2, 2021.
Presentation from the workshop 'Informing and Enabling a Climate Resilient Ireland”' - held 23 March 2012. This event launched 2 EPA Climate Change Research Programme reports:
CCRP9 'Ireland adapts to Climate Change' and CCRP10 'Integrating Climate Change Adaptation into Sectoral Policies in Ireland'
This deals with the assessment of several parameterizations of longwave radiation. The parametes were calibrated using a calibration tool on Ameriflux data.
The climate and earth sciences have recently undergone a rapid transformation from a data-poor
to a data-rich environment. In particular, massive amount of data about Earth and its
environment is now continuously being generated by a large number of Earth observing satellites
as well as physics-based earth system models running on large-scale computational platforms.
These massive and information-rich datasets offer huge potential for understanding how the
Earth's climate and ecosystem have been changing and how they are being impacted by humans’
actions. This talk will discuss various challenges involved in analyzing these massive data sets
as well as opportunities they present for both advancing machine learning as well as the science
of climate change in the context of monitoring the state of the tropical forests and surface water
on a global scale.
An Argo based estimate of Oxygen (O_2) at 150 m is presented for the Southern Ocean (SO) from T/S, O_2 Argo profiles collected during 2008-2012. The method is based on supervised machine learning, i.e. Random Forest (RF) regression, and provides an estimate for O_2 on gridded Argo T/S fields. Results show that the Southern Ocean State Estimate (SOSE) and the World Ocean Atlas 2013 climatology may overestimate annual mean O_2 in the SO, both on a global and basin scale. A large regional bias is found east of Argentina, where high O_2 values in the Argo based estimate are closer to the coast compared to other products. SOSE may also underestimate the annual cycle of O_2. Regions where the RF method does not perform well
(e.g. eastern boundaries) are identified comparing the actual SOSE O_2 fields to the RF estimate from model profiles co-located with observations. The RF based method presented here has the potential to improve our understanding of O_2 annual mean fields and variability from available (sparse) O_2 measurements. Also, it may guide the design of future enhancements to the current array of O_2 profiling floats, and prove effective for other biogeochemical variables (e.g.
nutrients and carbon).
CSP Training series : solar resource assessment 2/2Leonardo ENERGY
Fifth session of the 2nd Concentrated Solar Power Training dedicated to solar resource assessment.
* DNI Variability, Frequency Distributions
* Typical Meteorological Years
* DNI measurements: broadband vs. spectral, and their limitations
* What is circumsolar radiation and why should we care in CSP/CPV?
* How much diffuse irradiance can be used in concentrators?
* How to measure and model the circumsolar irradiance?
* Spectral irradiance standards and their use for PV/CPV rating
* The AM1.5 direct standard spectrum: Why did it change? Why AM1.5?
* Use of the SMARTS radiative code to evaluate clear-sky spectral irradiances
* Sources of measured spectral irradiance data
* Spectral effects on silicon and multijunction cells and their dependence on climate
Effects of Climate Change on Hydrology and Hydropower Systems in the Italian ...pietro richelli
In this study we assess the impact of climate change on the hydro- logical cycle of an Alpine catchment and on the management of hy- dropower systems. We apply the traditional climate change impact study approach, known in the literature as “scenario-based” approach, to the case study of Lake Como catchment. The “scenario-based” ap- proach consists in employing a modelling chain, which comprises the definition of Green House Gases emission scenarios, the simulation of climate models and hydrological models, and the simulation of the impact on water resources.
We take into account an ensemble of climate scenarios, comprising two Representative Concentration Pathways (RCPs), seven General Circulation Models (GCMs) and five Regional Circulation Models (RCMs). The analysis of the climate scenarios on the domain of inter- est shows an increase in temperature and a seasonal shift in precip- itation, causing drier summers and more rainy winters. We apply a statistical downscaling to the climate scenarios in order to match the adequate spatial resolution needed for hydrological modelling. We adopt Topkapi-ETH, a physically-based and fully distributed hydro- logical model, to reproduce the response of the catchment hydrology to climate change. The employment of a spatially distributed model is due to the possibility of assessing the impact of climate change on different areas of the catchment. Moreover, Topkapi-ETH allows to simulate anthropogenic infrastructures such as reservoirs and river diversions, which are widely present in the Lake Como catchment. The simulation results over the XXI century scenario show a seasonal shift in the hydrological cycle, with lower flow in summer, higher flow in winter, and an earlier snowmelt peak. This results in different patterns of storage building in the Alpine hydropower reservoirs. Finally, we analyze the uncertainty on hydro-climatic variables asso- ciated to climate modelling. Results show that the uncertainty related to the choice of the GCM is the most critical, but comparable to the one of the RCM. The choice of the RCP is generally less crucial for short lead times, but it increases in relative terms for longer lead times.
This deals with the assessment of several parameterizations of longwave radiation. The parametes were calibrated using a calibration tool on Ameriflux data.
The climate and earth sciences have recently undergone a rapid transformation from a data-poor
to a data-rich environment. In particular, massive amount of data about Earth and its
environment is now continuously being generated by a large number of Earth observing satellites
as well as physics-based earth system models running on large-scale computational platforms.
These massive and information-rich datasets offer huge potential for understanding how the
Earth's climate and ecosystem have been changing and how they are being impacted by humans’
actions. This talk will discuss various challenges involved in analyzing these massive data sets
as well as opportunities they present for both advancing machine learning as well as the science
of climate change in the context of monitoring the state of the tropical forests and surface water
on a global scale.
An Argo based estimate of Oxygen (O_2) at 150 m is presented for the Southern Ocean (SO) from T/S, O_2 Argo profiles collected during 2008-2012. The method is based on supervised machine learning, i.e. Random Forest (RF) regression, and provides an estimate for O_2 on gridded Argo T/S fields. Results show that the Southern Ocean State Estimate (SOSE) and the World Ocean Atlas 2013 climatology may overestimate annual mean O_2 in the SO, both on a global and basin scale. A large regional bias is found east of Argentina, where high O_2 values in the Argo based estimate are closer to the coast compared to other products. SOSE may also underestimate the annual cycle of O_2. Regions where the RF method does not perform well
(e.g. eastern boundaries) are identified comparing the actual SOSE O_2 fields to the RF estimate from model profiles co-located with observations. The RF based method presented here has the potential to improve our understanding of O_2 annual mean fields and variability from available (sparse) O_2 measurements. Also, it may guide the design of future enhancements to the current array of O_2 profiling floats, and prove effective for other biogeochemical variables (e.g.
nutrients and carbon).
CSP Training series : solar resource assessment 2/2Leonardo ENERGY
Fifth session of the 2nd Concentrated Solar Power Training dedicated to solar resource assessment.
* DNI Variability, Frequency Distributions
* Typical Meteorological Years
* DNI measurements: broadband vs. spectral, and their limitations
* What is circumsolar radiation and why should we care in CSP/CPV?
* How much diffuse irradiance can be used in concentrators?
* How to measure and model the circumsolar irradiance?
* Spectral irradiance standards and their use for PV/CPV rating
* The AM1.5 direct standard spectrum: Why did it change? Why AM1.5?
* Use of the SMARTS radiative code to evaluate clear-sky spectral irradiances
* Sources of measured spectral irradiance data
* Spectral effects on silicon and multijunction cells and their dependence on climate
Effects of Climate Change on Hydrology and Hydropower Systems in the Italian ...pietro richelli
In this study we assess the impact of climate change on the hydro- logical cycle of an Alpine catchment and on the management of hy- dropower systems. We apply the traditional climate change impact study approach, known in the literature as “scenario-based” approach, to the case study of Lake Como catchment. The “scenario-based” ap- proach consists in employing a modelling chain, which comprises the definition of Green House Gases emission scenarios, the simulation of climate models and hydrological models, and the simulation of the impact on water resources.
We take into account an ensemble of climate scenarios, comprising two Representative Concentration Pathways (RCPs), seven General Circulation Models (GCMs) and five Regional Circulation Models (RCMs). The analysis of the climate scenarios on the domain of inter- est shows an increase in temperature and a seasonal shift in precip- itation, causing drier summers and more rainy winters. We apply a statistical downscaling to the climate scenarios in order to match the adequate spatial resolution needed for hydrological modelling. We adopt Topkapi-ETH, a physically-based and fully distributed hydro- logical model, to reproduce the response of the catchment hydrology to climate change. The employment of a spatially distributed model is due to the possibility of assessing the impact of climate change on different areas of the catchment. Moreover, Topkapi-ETH allows to simulate anthropogenic infrastructures such as reservoirs and river diversions, which are widely present in the Lake Como catchment. The simulation results over the XXI century scenario show a seasonal shift in the hydrological cycle, with lower flow in summer, higher flow in winter, and an earlier snowmelt peak. This results in different patterns of storage building in the Alpine hydropower reservoirs. Finally, we analyze the uncertainty on hydro-climatic variables asso- ciated to climate modelling. Results show that the uncertainty related to the choice of the GCM is the most critical, but comparable to the one of the RCM. The choice of the RCP is generally less crucial for short lead times, but it increases in relative terms for longer lead times.
Just a year ago, Pew Internet & American Life Project report that nearly 113 million Americans were using the Internet to gather health-related information. It's not news that the Internet has become a magnet for people looking for quick answers when it comes to health issues, whether or not the results of searches are providing them with the best information on a regular basis.
Financial Relations Board (FRB), an MWW Group company, has been a leader in financial communications and investor relations for more than 45 years and employs professionals and executives with an average of 18 years financial experience. Their background includes work as investment bankers, analysts, portfolio managers, business reporters/editors and financial communications practitioners.
From Employee Communications to Workforce EngagementMWWPR
In today’s environment, the need to engage and activate employees is paramount. In order to achieve the necessary and desired business outcomes, companies must move from the static, one-way message delivery that has traditionally driven the practice of Employee Communications to a more dynamic form of communication: Employee Engagement.
StreamConnect, premiata come migliore piattaforma digitale per il webcast allo Streaming Media Europe 2012, consente in semplici passi di realizzare qualsiasi funzionalità richiesta per i tuoi webinar, congressi, convegni o eventi corporate.
Climate change is projected to impact drastically in southern African during the 21st century
under low mitigation futures (Niang et al., 2014). African temperatures are projected to rise
rapidly, in the subtropics at least at 1.5 times the global rate of temperature increase (James
and Washington, 2013; Engelbrecht et al., 2015). Moreover, the southern African region is
projected to become generally drier under enhanced anthropogenic forcing (Christensen et
al., 2007; Engelbrecht et al., 2009; James and Washington, 2013; Niang et al., 2014). These
changes in temperature and rainfall patterns will plausibly have a range of impacts in South
Africa, including impacts on energy demand (in terms of achieving human comfort within
buildings and factories), agriculture (e.g. reductions of yield in the maize crop under higher
temperatures and reduced soil moisture), livestock production (e.g. higher cattle mortality as
a result of oppressive temperatures) and water security (through reduced rainfall and
enhanced evapotranspiration) (Engelbrecht et al., 2015).
Presentation of the results of Climate Modeling component on the CIAT-IDB project "Climate Change Vulnerability in the Agricultural Sector in Latinm America and the Caribbean"
This presentation was delivered at the third Asia-Pacific Forestry Week 2016, in Clark Freeport Zone, Philippines.
The five sub-thematic streams at APFW 2016 included:
Pathways to prosperity: Future trade and markets
Tackling climate change: challenges and opportunities
Serving society: forestry and people
New institutions, new governance
Our green future: green investment and growing our natural assets
Summary of key findings of "Climate Change 2013: The Physical Science Basis, Working Group I contribution to the IPCC 5th Assessment Report" by Matt Collins, University of Exeter, UK
SICCME open session, 17 September 2014, ICES Annual Science Conference, A Coruña, Spain
"Understanding the Carbon Cycle: Processes, Human Impacts, and Strategies for...MMariSelvam4
The carbon cycle is a critical component of Earth's environmental system, governing the movement and transformation of carbon through various reservoirs, including the atmosphere, oceans, soil, and living organisms. This complex cycle involves several key processes such as photosynthesis, respiration, decomposition, and carbon sequestration, each contributing to the regulation of carbon levels on the planet.
Human activities, particularly fossil fuel combustion and deforestation, have significantly altered the natural carbon cycle, leading to increased atmospheric carbon dioxide concentrations and driving climate change. Understanding the intricacies of the carbon cycle is essential for assessing the impacts of these changes and developing effective mitigation strategies.
By studying the carbon cycle, scientists can identify carbon sources and sinks, measure carbon fluxes, and predict future trends. This knowledge is crucial for crafting policies aimed at reducing carbon emissions, enhancing carbon storage, and promoting sustainable practices. The carbon cycle's interplay with climate systems, ecosystems, and human activities underscores its importance in maintaining a stable and healthy planet.
In-depth exploration of the carbon cycle reveals the delicate balance required to sustain life and the urgent need to address anthropogenic influences. Through research, education, and policy, we can work towards restoring equilibrium in the carbon cycle and ensuring a sustainable future for generations to come.
Artificial Reefs by Kuddle Life Foundation - May 2024punit537210
Situated in Pondicherry, India, Kuddle Life Foundation is a charitable, non-profit and non-governmental organization (NGO) dedicated to improving the living standards of coastal communities and simultaneously placing a strong emphasis on the protection of marine ecosystems.
One of the key areas we work in is Artificial Reefs. This presentation captures our journey so far and our learnings. We hope you get as excited about marine conservation and artificial reefs as we are.
Please visit our website: https://kuddlelife.org
Our Instagram channel:
@kuddlelifefoundation
Our Linkedin Page:
https://www.linkedin.com/company/kuddlelifefoundation/
and write to us if you have any questions:
info@kuddlelife.org
WRI’s brand new “Food Service Playbook for Promoting Sustainable Food Choices” gives food service operators the very latest strategies for creating dining environments that empower consumers to choose sustainable, plant-rich dishes. This research builds off our first guide for food service, now with industry experience and insights from nearly 350 academic trials.
Natural farming @ Dr. Siddhartha S. Jena.pptxsidjena70
A brief about organic farming/ Natural farming/ Zero budget natural farming/ Subash Palekar Natural farming which keeps us and environment safe and healthy. Next gen Agricultural practices of chemical free farming.
Diabetes is a rapidly and serious health problem in Pakistan. This chronic condition is associated with serious long-term complications, including higher risk of heart disease and stroke. Aggressive treatment of hypertension and hyperlipideamia can result in a substantial reduction in cardiovascular events in patients with diabetes 1. Consequently pharmacist-led diabetes cardiovascular risk (DCVR) clinics have been established in both primary and secondary care sites in NHS Lothian during the past five years. An audit of the pharmaceutical care delivery at the clinics was conducted in order to evaluate practice and to standardize the pharmacists’ documentation of outcomes. Pharmaceutical care issues (PCI) and patient details were collected both prospectively and retrospectively from three DCVR clinics. The PCI`s were categorized according to a triangularised system consisting of multiple categories. These were ‘checks’, ‘changes’ (‘change in drug therapy process’ and ‘change in drug therapy’), ‘drug therapy problems’ and ‘quality assurance descriptors’ (‘timer perspective’ and ‘degree of change’). A verified medication assessment tool (MAT) for patients with chronic cardiovascular disease was applied to the patients from one of the clinics. The tool was used to quantify PCI`s and pharmacist actions that were centered on implementing or enforcing clinical guideline standards. A database was developed to be used as an assessment tool and to standardize the documentation of achievement of outcomes. Feedback on the audit of the pharmaceutical care delivery and the database was received from the DCVR clinic pharmacist at a focus group meeting.
UNDERSTANDING WHAT GREEN WASHING IS!.pdfJulietMogola
Many companies today use green washing to lure the public into thinking they are conserving the environment but in real sense they are doing more harm. There have been such several cases from very big companies here in Kenya and also globally. This ranges from various sectors from manufacturing and goes to consumer products. Educating people on greenwashing will enable people to make better choices based on their analysis and not on what they see on marketing sites.
Willie Nelson Net Worth: A Journey Through Music, Movies, and Business Venturesgreendigital
Willie Nelson is a name that resonates within the world of music and entertainment. Known for his unique voice, and masterful guitar skills. and an extraordinary career spanning several decades. Nelson has become a legend in the country music scene. But, his influence extends far beyond the realm of music. with ventures in acting, writing, activism, and business. This comprehensive article delves into Willie Nelson net worth. exploring the various facets of his career that have contributed to his large fortune.
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Introduction
Willie Nelson net worth is a testament to his enduring influence and success in many fields. Born on April 29, 1933, in Abbott, Texas. Nelson's journey from a humble beginning to becoming one of the most iconic figures in American music is nothing short of inspirational. His net worth, which estimated to be around $25 million as of 2024. reflects a career that is as diverse as it is prolific.
Early Life and Musical Beginnings
Humble Origins
Willie Hugh Nelson was born during the Great Depression. a time of significant economic hardship in the United States. Raised by his grandparents. Nelson found solace and inspiration in music from an early age. His grandmother taught him to play the guitar. setting the stage for what would become an illustrious career.
First Steps in Music
Nelson's initial foray into the music industry was fraught with challenges. He moved to Nashville, Tennessee, to pursue his dreams, but success did not come . Working as a songwriter, Nelson penned hits for other artists. which helped him gain a foothold in the competitive music scene. His songwriting skills contributed to his early earnings. laying the foundation for his net worth.
Rise to Stardom
Breakthrough Albums
The 1970s marked a turning point in Willie Nelson's career. His albums "Shotgun Willie" (1973), "Red Headed Stranger" (1975). and "Stardust" (1978) received critical acclaim and commercial success. These albums not only solidified his position in the country music genre. but also introduced his music to a broader audience. The success of these albums played a crucial role in boosting Willie Nelson net worth.
Iconic Songs
Willie Nelson net worth is also attributed to his extensive catalog of hit songs. Tracks like "Blue Eyes Crying in the Rain," "On the Road Again," and "Always on My Mind" have become timeless classics. These songs have not only earned Nelson large royalties but have also ensured his continued relevance in the music industry.
Acting and Film Career
Hollywood Ventures
In addition to his music career, Willie Nelson has also made a mark in Hollywood. His distinctive personality and on-screen presence have landed him roles in several films and television shows. Notable appearances include roles in "The Electric Horseman" (1979), "Honeysuckle Rose" (1980), and "Barbarosa" (1982). These acting gigs have added a significant amount to Willie Nelson net worth.
Television Appearances
Nelson's char
Characterization and the Kinetics of drying at the drying oven and with micro...Open Access Research Paper
The objective of this work is to contribute to valorization de Nephelium lappaceum by the characterization of kinetics of drying of seeds of Nephelium lappaceum. The seeds were dehydrated until a constant mass respectively in a drying oven and a microwawe oven. The temperatures and the powers of drying are respectively: 50, 60 and 70°C and 140, 280 and 420 W. The results show that the curves of drying of seeds of Nephelium lappaceum do not present a phase of constant kinetics. The coefficients of diffusion vary between 2.09.10-8 to 2.98. 10-8m-2/s in the interval of 50°C at 70°C and between 4.83×10-07 at 9.04×10-07 m-8/s for the powers going of 140 W with 420 W the relation between Arrhenius and a value of energy of activation of 16.49 kJ. mol-1 expressed the effect of the temperature on effective diffusivity.
Summary of the Climate and Energy Policy of Australia
Pattern scaling using ClimGen
1. Pa#ern
scaling
using
ClimGen:
User
needs
Changing
precipita0on
variability
Interac0on
between
global
&
regional
responses
Tim
Osborn
&
Craig
Wallace
Clima&c
Research
Unit,
University
of
East
Anglia
April
2014
Pa:ern
scaling,
climate
model
emulators
&
their
applica&on
to
the
new
scenario
process
NCAR,
Boulder,
Colorado
Work
supported
by
TOPDAD
&
HELIX
EU
projects
2. Pattern scaling: meeting user needs
Key
requirements:
• Explore
spread
(uncertainty?)
of
climate
projec0ons
• Pre-‐CMIP3,
CMIP3,
CMIP5
mul0-‐model,
QUMP
perturbed
parameters
• Generate
projec0ons
for
un-‐simulated
scenarios
User
needs:
• Iden0cal
formats
for
all
scenarios
(&
observa0ons)
• Flexible
temporal,
seasonal
and
geographic
windowing/averaging
3. Pattern scaling: meeting user needs
Example
na0onal
average
summer
T
&
P
changes
Pink
=
CMIP3
distribu:on
Open
symbols
=
CMIP3
models
Key
requirements:
• Explore
spread
(uncertainty?)
of
climate
projec0ons
• Pre-‐CMIP3,
CMIP3,
CMIP5
mul0-‐model,
QUMP
perturbed
parameters
• Generate
projec0ons
for
un-‐simulated
scenarios
Natural variability
ΔT = 0.5, 1.5, 3
For global warming ΔT = 3 K (left panel) or 0.5, 1.5 and 3 K (right panel)
Based on Osborn et al. (under review) Climatic Change
4. Pattern scaling: meeting user needs
Example
na0onal
average
summer
T
&
P
changes
Pink
=
CMIP3
distribu:on
Open
symbols
=
CMIP3
models
Brown
=
CMIP5
distribu:on
Solid
symbols
=
CMIP5
models
Key
requirements:
• Explore
spread
(uncertainty?)
of
climate
projec0ons
• Pre-‐CMIP3,
CMIP3,
CMIP5
mul0-‐model,
QUMP
perturbed
parameters
• Generate
projec0ons
for
un-‐simulated
scenarios
Natural variability
ΔT = 0.5, 1.5, 3
For global warming ΔT = 0.5, 1.5 and 3 K (right panel)
Based on Osborn et al. (under review) Climatic Change
5. Pattern scaling: meeting user needs
Key
requirements:
• Explore
spread
(uncertainty?)
of
climate
projec0ons
• Pre-‐CMIP3,
CMIP3,
CMIP5
mul0-‐model,
QUMP
perturbed
parameters
• Generate
projec0ons
for
un-‐simulated
scenarios
Natural variability
ΔT = 0.5, 1.5, 3
Example
na0onal
average
summer
T
&
P
changes
Pink
=
CMIP3
distribu:on
Open
symbols
=
CMIP3
models
Brown
=
CMIP5
distribu:on
Solid
symbols
=
CMIP5
models
Blue
=
QUMP
distribu:on
Black
le#ers
=
QUMP
models
For global warming ΔT = 0.5, 1.5 and 3 K (right panel)
Based on Osborn et al. (under review) Climatic Change
6. Pattern scaling: meeting user needs
Mul0ple
climate
variables
(all
monthly
means,
mostly
land-‐only):
• Near-‐surface
temperature
(mean,
min,
max,
DTR)
• Precipita0on
&
wet-‐day
frequency
• Cloud-‐cover
(can
es0mate
sunshine
hours
or
radia0on
variables)
• Vapour
pressure
(can
es0mate
other
humidity
variables)
• SST
is
currently
the
only
variable
provided
over
the
oceans
User
needs:
more
derived
variables,
extreme
events
&
variability
• Hea0ng
&
cooling
degree
days
(HDD
&
CDD)
• Poten0al
evapotranspira0on
(PET,
e.g.
from
Penman-‐Mon0eth)
• Drought
indicators
(e.g.
Standardised
Precipita0on-‐Evapotranspira0on
Index,
SPEI)
How
to
deal
with
climate
(and
weather)
variability?
7. Climate variability in pattern scaling: (1) use observations
Sample
from
observed
variability:
• Realis0c
for
present-‐day
• But
doesn’t
change
when
the
mean
climate
changes
Design
sampling
to
allow
the
separa0on
of
climate
change
and
natural
variability
effects
• Use
mul0ple
0me-‐shided
sequences
instead
of
single
observed
sequence
8. Climate variability in pattern scaling: (1) use observations
Sample
from
observed
variability:
• Realis0c
for
present-‐day
• But
doesn’t
change
when
the
mean
climate
changes
Design
sampling
to
allow
the
separa0on
of
climate
change
and
natural
variability
effects
• Use
mul0ple
0me-‐shided
sequences
instead
of
single
observed
sequence
9. Climate variability in pattern scaling: (1) use observations
• Or
generate
slices
represen0ng
climate+variability
for
specific
amounts
of
ΔT
Fig. S3 of Osborn et al. (under review) Climatic Change
10. Climate variability in pattern scaling: (2) perturb observations
Pahern-‐scale
higher
moments
(e.g.
standard
devia0on,
skew)
• We
divide
GCM
monthly
precipita0on
0meseries
by
low-‐pass
filter
• Represent
the
high-‐frequency
devia0ons
with
a
gamma
distribu0on
• Scale
changes
in
gamma
shape
parameter
with
ΔT
Fig. 1 of Osborn et al. (under review) Climatic Change
Relativechangein
11. Climate variability in pattern scaling: (2) perturb observations
Example
applica0on
• SE
England
grid
cell,
HadCM3
GCM,
July
precipita0on
• For
ΔT
=
3°C,
pahern-‐scaling
gives
45%
reduc0on
in
mean
precipita0on
• But
also
62%
reduc0on
in
gamma
shape
param.
of
monthly
precipita0on
Fig. 1 of Osborn et al. (under review) Climatic Change
Observed sequence
Sequence x 0.55 Sequence x 0.55
Sequence x 0.55 &
perturbed to have 62% lower shape
12. Is there agreement in GCM-simulated changes of variability?
• Mul0-‐model
mean
of
22
CMIP3
GCMs
• Normalised
change
in
gamma
shape
of
July
precipita0on
Units: % change / K
Fig. 1 of Osborn et al. (under review) Climatic Change
13. Is there agreement in GCM-simulated changes of variability?
• Mul0-‐model
mean
of
20
CMIP5
GCMs
• Normalised
change
in
gamma
shape
of
July
precipita0on
Units: % change / K
Based on Osborn et al. (under review) Climatic Change
14. Is there agreement in GCM-simulated changes of variability?
• Mul0-‐model
agreement
of
22
CMIP3
GCMs
• Frac0on
of
models
showing
increased
gamma
shape
of
July
precipita0on
Units: fraction
Based on Osborn et al. (under review) Climatic Change
15. Is there agreement in GCM-simulated changes of variability?
• Mul0-‐model
agreement
of
20
CMIP5
GCMs
• Frac0on
of
models
showing
increased
gamma
shape
of
July
precipita0on
Units: fraction
Based on Osborn et al. (under review) Climatic Change
16. Transform
observed rainfall
series by factors
given by range of
ΔT from 0 to 6K
Count frequency
of short droughts
in each
transformed
series
Estimate
uncertainty
UK drought
frequency vs.
global ΔT
Does pattern-scaling emulate GCM/RCM behaviour?
HadCM3
GCM
HadRM3
RCM
17. Can we treat global and regional changes independently?
• Separa0on
into
global
ΔT
&
regional
paherns
is
convenient
• Especially
for
the
treatment
of
uncertain0es
18. Can we treat global and regional changes independently?
• Separa0on
into
global
ΔT
&
regional
paherns
is
convenient
• Especially
for
the
treatment
of
uncertain0es
Simple example:
Estimating conditional PDFs of UK drought frequency,
using HadRM3 RCM pattern-scaling results and the
Wigley & Raper (2001) PDFs of ΔT
19. Simple example:
Estimating conditional PDFs of UK drought frequency,
using HadRM3 RCM pattern-scaling results and the
Wigley & Raper (2001) PDFs of ΔT
Can we treat global and regional changes independently?
• Separa0on
into
global
ΔT
&
regional
paherns
is
convenient
• Especially
for
the
treatment
of
uncertain0es
20. Estimating conditional PDFs of UK drought frequency
Can we treat global and regional changes independently?
• Separa0on
into
global
ΔT
&
regional
paherns
is
convenient
• Especially
for
the
treatment
of
uncertain0es
21. Can we treat global and regional changes independently?
• Separa0on
into
global
ΔT
&
regional
paherns
is
convenient
• Especially
for
the
treatment
of
uncertain0es
• But
can
I
combine
ΔT
derived
from
a
par0cular
climate
sensi0vity
with
any
of
the
GCM
paherns?
• Or
are
the
normalised
change
paherns
of
high
sensi0vity
GCMs
systema0cally
different
from
those
of
low
sensi0vity
GCMs?
22. Rank
correla0on
between
temperature
and
ECS
for
CMIP3
Are the normalised change patterns of high sensitivity GCMs
systematically different from those of low sensitivity GCMs?
Osborn et al. (in preparation)
Rank correlation for 22 GCMs
>80% significant correlations shown
23. Rank
correla0on
between
temperature
and
ECS
for
QUMP
Are the normalised change patterns of high sensitivity GCMs
systematically different from those of low sensitivity GCMs?
Osborn et al. (in preparation)
Rank correlation for 17 GCMs
>80% significant correlations shown
24. Rank
correla0on
between
temperature
and
ECS
for
CMIP3,
CMIP5
&
QUMP
Are the normalised change patterns of high sensitivity GCMs
systematically different from those of low sensitivity GCMs?
Osborn et al. (in preparation)
Rank correlation for 52 GCMs
>80% significant correlations shown
25. Conclusions: meeting user needs with pattern scaling
Exploring
the
uncertainty
of
climate
projec0ons:
• Given
wide
mul0-‐model
ensemble
ranges,
sufficient
to
approximately
emulate
plume
of
future
regional
changes
Increasing
demand
for
emula0on
to
include
variability
&
represent
extremes:
• Need
to
treat
variability
with
care,
sufficient
sampling
etc.
• Can
pahern-‐scale
higher
order
parameters
(e.g.
standard
devia0on,
skew)
and
perturb
observed
variability
accordingly
• More
complicated
changes
(e.g.
shid
in
ENSO
behaviour)
cannot,
however,
be
captured
Systema0c
differences
between
normalised
paherns
from
low
and
high
sensi0vity
models
complicates
the
separate
treatment
of
uncertainty
in
global
ΔT
and
regional
climate
change