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Meeting Report
W108 - Climate Change and
the Built Environment
2006
Report of the 5th Meeting on Climate
Change and the Built Environment
1 June 2006
Weimar, Germany
CIB W108 meeting 2006 The 2006 CIB W108 meeting on Climate Change and the Built Environment was held in Weimar on 1 June. 20 participants from the UK, France, Slovakia and Germany met at the chair of Prof. Kornadt at Bauhaus-University Weimar which is a college (university?) for architects and civil engineers named after the famous Bauhaus school (1919 - 1933). The emphasis of this year's meeting, chaired by Prof. Oliver Kornadt and assisted by Dr Sabine Hoffmann, was on the generation of weather data for future climate change and their use for building simulation. An interesting exchange between British and German experts on the topic of climate data on a regional scale was one of the main benefits of the meeting with the prospect for future collaboration. The practitioners and consultants that attended the meeting pointed out that future, realistic weather data for building simulation in the design process would improve the design and adaptation of buildings to global warming. Besides adaptation to higher indoor temperatures the meeting also considered the mitigation of climate change effects. The building sector as a huge producer of carbon emissions should adopt a more CO2 related perception in order to increase the sustainability of the built environment. The next meeting of CIB W108 will be held in Spain in October 2007 when the full IPCC Fourth Assessment Report should be available for discussion. Two W108 members were involved in writing the buildings chapter.
CIB W108, Weimar
Visualisationin Climate and Earth System Research
Michael BöttingerDeutsches Klimarechenzentrum
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research2
AgendaAgenda
• About DKRZ• From Climate to Earth System Models• Data structures and formats• Advanced Visualization• Challenges and perspectives
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research3
About DKRZ (1)About DKRZ (1)
• DKRZ: Deutsches Klimarechenzentrum= German High Performance Computing Centre for Climate and Earth
System Research
DKRZ provides state-of-the-art supercomputing, data servicesand other associated services to the German scientific community to conduct top of the line Earth System and Climate Modeling. Limited non-for-profit company: MPG, Univ. Hamburg, AWI, GKSS
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research4
About DKRZ (2)About DKRZ (2)
• DKRZ’s current HPC Hardware• NEC SX-6 Vector Supercomputer (24 nodes, 192
CPUs, 1,5 TB Mem., installed 2002)• 8 NEC TX7 Systems (total of ~140 IA-64 CPUs)• Shared File system (NEC-GFS), ~ 100 TByte• 6 StorageTek Silos, total capacity ~ 6 PetaByte
• … dedicated to climate research!
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research5
About DKRZ (3): Data Archive 1992About DKRZ (3): Data Archive 1992--20042004
[ TB ]
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research6
Complexity of the Climate System (1)Complexity of the Climate System (1)
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research7
Complexity of the Climate System (2)Complexity of the Climate System (2)
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research8
MomentumEnergyWater
Land Surface
Coupler
Sun Atmosphere
Ocean
concentrations(GHG, SO4)
Source: MPI-M
Physical climate modelPhysical climate model
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research9
CO2DMSMomentumEnergyWater
Ocean
Atmosphere
Emissions
Dust
Sun
Dynamics Aerosols Chemistry
Land surfaceHydrologyPhotosynthesisPhenologyRespiration
DynamicsSea iceBiologyChemistry
Run off
Under Development (at MPIUnder Development (at MPI--M): M): Earth System ModelEarth System Model
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research10
Model ResolutionModel Resolution
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research11
Grid ConfigurationsGrid Configurations
Example: curvilinear 302x132 grid configuration used with the HOPE-C ocean model.
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research12
Time ScalesTime Scales
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research13
Ensemble SimulationsEnsemble Simulations
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research14
Data Structures (1)Data Structures (1)
• What does this all mean in regard to the visualization of the data?
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research15
Data Structures (2)Data Structures (2)• per model
• 3-D gridded data• Some quantities only 2-D• Multivariate: scalar / vector quantities• Grids: Rectilinear, curvilinear, triangle
different grids for scalars / vectors• Time dependence
• per experiment• Ensembles of…• … data of different subsystem models…• …on different grids …• … with different time steps
• Additional Issues• Geographical mapping• Ocean: Special values to mask out the land
• Formats: GRIB, netCDF, IEEE binary• Conventions for meta data (e.g.NetCDF/CF)
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research16
Advanced VisualizationAdvanced Visualization
• What is “Low-End”-Visualization?
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research17
Low End Example: Animation with Low End Example: Animation with vcdatvcdatECHAM5 T159
480 x 240,
104 Time Steps
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research18
Advanced VisualizationAdvanced Visualization
• My definition of “Advanced Visualization”• Anything beyond “Low End”• 3-D, timed dependent, multiple variables• Data size (e.g. too big for PCs)• New methods• “Beautified” standard methods• Stereoscopic, VR
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research19
Examples (1) one 2Examples (1) one 2--D scalarD scalarECHAM5 /
MPI-OM
Software:
AVS/Express
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research20
Examples (2): one 2Examples (2): one 2--D scalarD scalarMOM - 1/12°
1045 x 624
Vel. at 100m
396 Timesteps3 day interval
Software:
AVS/Express
(Visualization: J. Biercamp, DKRZ
Data: Univ. Kiel)
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research21
Examples (3): two 2Examples (3): two 2--D scalarsD scalarsMOM - 1/12°
1045 x 624
Temp. and Vel. at 100m
396 Timesteps3 day interval
Software:
AVS/Express
(Visualization: M. Böttinger, DKRZ
Data: Univ. Kiel)
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research22
Examples (4): two (four) 2Examples (4): two (four) 2--D scalars D scalars ECHAM5 / MPI-OM
T63 (~180km)
Software:
AVS/Express
Adobe After Effects
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research23
Cloud covercoloured bytemperature, specifichumidity and rain
= 4 scalars
401 x 271 x 20
108 time steps
Software: vis5d+
Examples (5): three 3Examples (5): three 3--D scalars, one 2D scalars, one 2--DD--scalarscalar
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research24
Examples (5): five 3Examples (5): five 3--D scalarsD scalarsECHAM5-HAM
T63 (~180km)
6-hourly
1 year
~ 10 Gbyte
Software:
AVS/Express
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research25
Advanced Visualization Advanced Visualization -- Resources at DKRZResources at DKRZ
• Services for DKRZ’s users• Viz-projects
• beyond standard requirements• cooperation with scientific users
• Support, Workshops, Documentation• Video recording and production
• Avid Media Composer• BetacamSP
• Visualization Software• 3D: Vis5d+, AVS/Express, NAG explorer,
Amira, Maya, OpenDX, vtk• 2D: GrADS, CDAT, NCAR Graphics,
Ferret, IDL(green: public domain software)
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research26
Advanced Visualization Advanced Visualization -- Resources at DKRZResources at DKRZ• Visualization-Hardware
• SGI Prism (4 CPUs, 2 Pipes, 8GB Mem)• SGI OpenGL Vizserver
• PC’s with OpenGL Cards (Win2k, Linux)• SGI Onyx2 IR (2 CPUs, 1 Pipe, 1 GB Mem)• Stereoscopic Displays (D4D, Projection)
• DKRZ acquires a “Visualization Server” in 2006• multiple 3-D-Gfx cards, multiple processors • Remote 3-D-Rendering• Efficient access to data archive• Much more memory, disk space• Stereo Back Projection System
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research27
Advanced Visualization at DKRZ Advanced Visualization at DKRZ -- ExperiencesExperiences
• Discrepancy: “what is possible” / “what is used”• 3D not used in publications (in Climate Research)• Possible reasons:
• 3D is more a qualitative way to visualize data• 3D is less reproducible• Harder to learn and use• Limited integration with post processing / analysis• Limited or missing support for native data formats• (Until recently) local visualization hardware required• Availability of suitable visualization solutions: PD / commercial
• But 3D is valuable to interactively visualize the data!• Understand the data• Communicate the scientific results: Presentations, public outreach
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research28
Challenges and PerspectivesChallenges and Perspectives
• Visualization Hardware• Automatic utilization of parallel hardware & compositing• Desktop VR
• Visualization Software• Availability on new HW platforms• Utilization of multiple cores/CPUs and multiple Gfx-cards • Support for many data formats and grid types• Which Visualization Software can deal with massive data?
• In-Core vs. Out-of-Core techniques• Ease of use!• Availability of (new) methods and algorithms
• Example: Shadows • Example: Flow Visualization
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research29
Challenges and PerspectivesChallenges and Perspectives
• Availability of (new) methods and algorithms• Example: Shadows enhance the depth perception
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research30
Challenges and PerspectivesChallenges and Perspectives
Example: Flow Visualization
usingLEA,
Stream Surfaces,LEA + Stream Surf.
(Erlebacher, Jobard,
Weisskopf, 2004)
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research31
ConclusionsConclusions
• Earth System Modeling: data size problem• Visualization
• might help to overcome this• Valuable to understand and communicate complex processes
• What is more limiting – SW or HW?• Visualization Hardware on a good way, but..
• Automatic parallelization / use of multiple pipes for applications desirable
• Visualization Software: some advances, but…• Data import still an issue• All systems have their strengths and weaknesses• usability• New methods and techniques?!
01.06.2006CIB W108 --- M. Böttinger, DKRZ: Visualisation in Climate and Earth System Research32
EndEnd
• Links and references:• http://www.dkrz.de/• „Das Erdsystem im Höchstleistungsrechner – Klimaprognosen“
by M. Böttinger, in “Max-Planck-Gesellschaft – Jahrbuch 2004”http://www.mpg.de/bilderBerichteDokumente/dokumentation/jahrbuch/2004/dkrz/forschungsSchwerpunkt/index.html
• “Visualization in Earth System Science”by M. Böttinger, M. Schultz, J. Biercamp, in “ACM SIGGRAPH Computer Graphics”, Volume 36 , Issue 4 (November 2002)http://www.csar.cfs.ac.uk/about/csarfocus/focus10/vis_earth.pdf
• „Visualisierung als Werkzeug zur Analyse von Klimasimulationsdaten“by M. Böttinger, V. Gülzow, J. Biercamp, in H.-D. Haasis, K.C. Ranze (Hg.) (1998): Umweltinformatik '98: Vernetzte Strukturen in Informatik, Umwelt und Wirtschafthttp://enviroinfo.isep.at/UI%2098/PDF%20-%20UI-98/710-721%20B%F6ttinger....pdf
Reliability of Climate Projections
Armin Bunde (Justus-Liebig Universität Giessen)
Coworkers: E. Koscielny-Bunde, J. Eichner, D. Rybski (Giessen)S. Havlin, D. Vjushin (Ramat-Gan, Israel)H. v. Storch (Geesthacht), H.J. Schellnhuber (Potsdam)
Challenges:
(a) Global warming and future climate
(b) Recent increase of hazardous floods
How the talk is organized:
1. Quantities of interest and correlation analysis
2. Long-term correlations in climate records
3. Evaluation of climate models by long-termcorrelations
1. Quantities of interest and correlationanalysis
days0 200 400 600 800 1000
-10
0
10
20
30
Prague
tem
pera
ture
[°C
]
0
200
400
600
800 Weser (Vlotho)
runo
ff [m
3 /s]
iii TT −=τ (seasonal detrending)
Ti
Ti
Correlation analysis: autocorrelation function
γ−−
=++ ∑ ττ
−=ττ≡ s
sNsC
sN
isiisii ~1)(
1)10( << γ
Problems:
For finite records the direct evaluation is not accurate for large s values.Trends superimposed on the data will lead to spurious results
Better: Fluctuation analysis (random walk analysis)
We consider the `profile´ ∑=
=i
jjiY
1
τ
determine its mean fluctuation F(s) in time windows of length scales s.
For uncorrelated records :
For long-term correlatedrecords:
2/1~)( ssF
2/1,~)( γαα −=ssF
and
αγγα 22,~)(~)( −=⇔ −ssCssF
2. Long-term correlations in climate records
Summary of the fluctuation exponents
Eichner et al, 2003, Rybski et al, 2003, Koscielny-Bunde et al, 1996, 1998, 2004
αγγα 22,~)(~)( −=⇔ −ssCssF
3. Evaluation of climate models by long-termcorrelations
The long-term correlations, in particular of the continental temperaturerecords, are a non-trivial test bed for the performance of the climatemodels. Climate models consider the climate system under the influence of external forcings:
Consider 2 models (reconstruction of the past):
(a) NCAR-PCM (BOULDER, Colorado)Natural forcings: sun (S) and volcanic (V) eruptionsAndropogenic forcings: greenhouse gas (G), ozone (Oz), sulfates (Su)
(b) ECHO-G (Hamburg)Natural forcings: sun (S) and volcanic (V) eruptionsAndropogenic forcings: greenhouse gas (G)
Summary: Continental Stations
Test of the performance at 16 continental stations (Vancouver, Cheyenne, Brookings, Chita, Luling, Tucson, Albany, Oxford, Prague, Kasan, Tashkent, Surgut, Jakutsk, Seoul, Sydney, Melbourne) and 16 sites in the Atlantic Ocean.
Summary: Ocean sites
Vjushin et al, 2004
1. NCAR-PCM model (Boulder, USA)
2. ECHO-G: Historical simulation (1000y)
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 1
COLOMBERT Morgane, SALAGNAC Jean-Luc, DIAB Youssef
The urban climate, a stakefor tomorrow?
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 2
The urban climate, a stake for tomorrow?
- Urban climate : description- Urban climate : explanation- Research on urban climate- Impact of climate change- Conclusion
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 3
Urban climate : descriptionEvolution of climatic parameters
Temperature evolution between 1800 and 1970 in Paris (Montsouris) (Source : Dettwiller, 1978)
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 4
Urban climate : descriptionEvolution of climatic parameters
Number of frost days during the twentieth century in Paris (Montsouris)(Source : www.meteo.fr)
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 5
Urban climate : descriptionEvolution of climatic parameters
Impact on others parameters : - Fog : In Paris (Montsouris) :
- Smog
- Duration of insolation
Days per year Years
107 1921-192511 1976-1980
(Source : www.freefoto.com)
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 6
Urban climate :descriptionDifference between urban and rural areas
(Source : www.gvrd.bc.ca)
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 7
Urban climate :descriptionDifference between urban and rural areas
Section NW-SE with the minimal temperatures in winter (1971-1980)
(Source : Colombert, 2005)
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Urban climate : descriptionDifference between urban and rural areas
Number of frost days (Source : Escourrou, 1986)
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Urban climate : descriptionDifference between urban and rural areas
Urban center 4 days
Urban parc 10 days
Suburbs nearthe town
30 days
Suburbs far away
50 days
Rural area 65 days
Number of days per year with fog in the region ofParis in 1976-1980 (Source : Cantat, 1986)
(Source : www.freefoto.com)
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 10
Urban climate : descriptionDifference between urban and rural areas
Paris Montsouris
1803 h
Villacoublay 1729 h
Versailles 1713 h
Trappes 1691 h
Gometz-la-Ville
1695 h
Yearly duration of insolation (1971-1980) (Source : Cantat, 1986)
Urban center
Rural area
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 11
Urban climate : descriptionDifference between urban and rural areas
Increase of rainfall because of urbanization (record of the 6th of June 1978). (Source : Escourrou)
Wind
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 12
Urban climate : descriptionDifference between urban and rural areas
The wind :> Decrease of the speed> More important Turbulence> Creation of local winds
(Source : http://www.islandnet.com/~see/weather/elements/citywind.htm)
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 13
Urban climate : explanation
Consequences of urban activitiesLocal modification of surfaces characteristicsExternal context
(Source : www.freefoto.com)
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 14
Urban climate : explanationConsequences of urban activities
Anthropogenic heat in the center of Paris :
Date Heat flux density(W.m-2)
1880 7 - 81978 601978 (Winter)
80 - 85
1978 (summer)
40 - 45
SUN = 210 W.m-2 (summer)= 42 W.m-2 (winter)
(Source : Dettwiller, 1978)(www.freefoto.com)
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 15
Urban climate : explanationLocal modification of surfaces characteristics
Albedo in the urban environment(Source : http://www.atmosphere.mpg.de/enid/0,55a304092d09/Climate_in_brief/-_Climate_in_Cities_2t9.html)
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 16
Urban climate : explanationLocal modification of surfaces characteristics
Capture of radiation by town in comparison with the case of rural area (Sacré, 1983)
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 17
Urban climate : explanationLocal modification of surfaces characteristics
Modification of water balance
Towncenter
Sensible heat
80 %
Latent heat
Sensible heat
Latent heat
20%
Rural area withvegetation
14%
86%
(Source : www.freefoto.com)
(Source : Cantat, 1993)
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 18
Urban climate : explanationExternal context
Schematic representation ofthe form of the air layer modified by a city :
(A) with steady regional wind
(B) in calm conditions
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 19
Research on urban climate
History Pioneer work by Luke Howard (1833)
Simulation : > to evaluate the influences of the parameters like albedo,
anthropogenic heat…> To conceive some modifications : more vegetation, albedo less
strong
Link with urban planning ?
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 20
Climate change impact
(Source : IPCC)
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 21
Climate change impact
Space conditioning demand [Danny Parker, FSEC (Florida Solar Energy Center) (http://www.iea.org/)]
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 22
Climate change impact
Climate change andits interactions withother global problems
Urban climate is not yetclearly addressed
(Source : « le défi de la Terre »)
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 23
Conclusion
Needs :> To prepare town to climate change
consequences> To take its particularities into account> To create efficient tools to help town-
decision makers to adapt to new conditions
(Source : www.freefoto.com)
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 24
The urban climate, a stake for tomorrow?
Thank you for your attention
Morgane Colombert C.S.T.B.
Services, Process, Innovation Center [email protected]
01/06/2006 | Services, Process, Innovation Center | CIB 108 – Weimar PAGE 25
Bibliography
CANTAT, Olivier. Influence de l’urbanisation sur le climat de l’agglomération parisienne. Physio-Géo, 1986, n 16, p. 25-40.
CANTAT, Olivier. Conséquences climatiques des variations du bilan d’énergie en région parisienne. Géographie physique et environnement, 1993, n 1, p. 19-36.
CANTAT, Olivier. L’îlot de chaleur urbain parisien selon les types de temps. Norois, 2004, n 191, p.75-102. COLOMBERT, Morgane. « Le climat urbain, un enjeu pour demain ». Mémoire de Master, Paris, Université de
Marne-la-Vallée, 2005. 91 p. DETTWILLER, J. L’évolution séculaire de la température à Paris. La Météorologie, 1978, n 13, p. 95-130. DUCHENE-MARULLAZ, Philippe. Recherche exploratoire en climatologie urbaine. CSTB, 1980. 86 p. ESCOURROU, Gisèle. Le climat de l’agglomération parisienne. L’information géographique, 1986, n 50, p. 96-102. ESCOURROU, Gisèle. La spécificité du climat de l’agglomération parisienne. Revue de Géographie de Lyon, 1990,
Vol. 65, n 2, p. 85-89. ONERC. Conséquences du réchauffement climatique sur les risques liés aux évènements météorologiques
extrêmes : sur la base des dernières connaissances scientifiques, quelle action locale ? : Colloque national sur le thème des élus face aux risques climatiques (Paris, juin 2003). Paris : ONERC, 2003, 70 p.
SACRE, Christian. Climatologie urbaine et climatologie de site. Ecole d’Architecture de Nantes, 1983, 38p. SENAT. La France et les français face à la canicule : les leçons d’une crise, rédigé par LETARD, V. FLANDRE, H.
LEPELTIER, S. SENAT, 2004. www.espere.net : Site de l’espere (Environmental Science Published for Everybody Round the Earth). www.ipcc.ch/ : site du GIEC (Groupe Intergouvernemental sur l’Evolution du Climat) ou IPCC en anglais
(Intergouvernmental Panel on Climate Change) avec notamment tous leurs rapports sur le changement climatique. www.meteo.fr : site de Météo France. Base data : Science Direct
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DWD-TRYs and HadRM3-TRYs -
a comparison for Munich and Weimar
Sabine Hoffmann, Oliver Kornadt
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Comparison of current weather dataDWD ↔ HadRM3
Method of generating Test Reference Yearsfrom Hadley-Model
Predicted Conditions of TRYs for 2020, 2050, 2080
Example of overheating hours when usingHadRM3-TRYs in building simulation
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German Test Reference Year (DWD-TRY)
Source: Germany’s National Meteorological Service Deutscher Wetterdienst (DWD)
Data sets for different regions in Germany:
TRY 4 corresponds to Weimar
TRY 13 corresponds to Munich
Only for current climate!
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Hadley regional climate model HadRM3
Source: Hadley Centre for Climate Prediction and Research, Met. Office, UKUniversity of Manchester
Whole of Europe in boxes of side 50 km
2 locations: Weimar, Munich
Simulated daily weather data for the periods 1960-1990 and 2070-2100 inclusive (A2-Scenario)
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WEIMAR: Average temperature and global radiation
spring summer autumn winter year
-5
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5
10
15
20
25
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35
40
45
50
55
tem
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-250
-200
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-50
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DWD-TRY Had RM 3
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WEIMAR: Maximum and minimum temperature
spring
summer
winter
autumn
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DWD-TRY Had RM3
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Phys
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Anforderungen an Gebäude durch langfristige Klimaveränderung
S. Hoffmann, O. Kornadt CIB Weimar, 01.06.2006
MUNICH: Average temperature and global radiation
spring summer autumn winter year
-5
0
5
10
15
20
25
30
35
40
45
50
55
tem
pera
ture
[°C
]
-300
-250
-200
-150
-100
-50
0
50
100
150
200
250
300
glob
al ra
diat
ion
[W/m
²]
DWD-TRY Had RM 3
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Anforderungen an Gebäude durch langfristige Klimaveränderung
S. Hoffmann, O. Kornadt CIB Weimar, 01.06.2006
MUNICH: Maximum and minimum temperature
autumn
winter
summer
spring
-30
-20
-10
0
10
20
30
40
50
tem
pera
ture
[°C
]
DWD-TRY Had RM 3
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Anforderungen an Gebäude durch langfristige Klimaveränderung
S. Hoffmann, O. Kornadt CIB Weimar, 01.06.2006
Comparison DWD-TRY ↔ HadRM3
- average temperature is reasonable
- HadRM3 implies higher global radiation
- WEIMAR: Min. temperatures same for both models (too high?), Max. temperatures are more realistic for DWD-TRY
- MUNICH: HadRM3 implies higher max. temperatures in summer and lower min. temperature in Winter
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Anforderungen an Gebäude durch langfristige Klimaveränderung
S. Hoffmann, O. Kornadt CIB Weimar, 01.06.2006
Method of generating HadRM3 TRYs:
- Search for the cumulative average monthout of 20 years x 3 runs
- Day ranking of typical month after maximum temperature- 1 typical year (12 months) for 1970
and 1 average year for 2080- Scaling factor interpolates values for 2020 (0.27)
and for 2050 (0.57)- Reordering daily values for 2020 and 2050 like in 1970
or like in 2080
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Anforderungen an Gebäude durch langfristige Klimaveränderung
S. Hoffmann, O. Kornadt CIB Weimar, 01.06.2006
WEIMAR: Average temperature HadRM3
yearautumn wintersummerspring-5
0
5
10
15
20
25
tem
pera
ture
[°C
]
1970 2020 2050 2080
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Phys
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Anforderungen an Gebäude durch langfristige Klimaveränderung
S. Hoffmann, O. Kornadt CIB Weimar, 01.06.2006
MUNICH: Average temperature HadRM3
yearautumn wintersummerspring-5
0
5
10
15
20
25
tem
pera
ture
[°C
]
1970 2020 2050 2080
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Anforderungen an Gebäude durch langfristige Klimaveränderung
S. Hoffmann, O. Kornadt CIB Weimar, 01.06.2006
WEIMAR: Maximum temperature HadRM3
autumn wintersummerspring5
10
15
20
25
30
35
40
45
50
tem
pera
ture
[°C
]
1970 2020 2050 2080
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Anforderungen an Gebäude durch langfristige Klimaveränderung
S. Hoffmann, O. Kornadt CIB Weimar, 01.06.2006
MUNICH: Maximum temperature HadRM3
spring summer winterautumn5
10
15
20
25
30
35
40
45
50
tem
pera
ture
[°C
]
1970 2020 2050 2080
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Anforderungen an Gebäude durch langfristige Klimaveränderung
S. Hoffmann, O. Kornadt CIB Weimar, 01.06.2006
0
10
20
30
40
50
60
30.7. 6.8. 13.8. 20.8. 27.8.
München 2080 Weimar 2080
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Anforderungen an Gebäude durch langfristige Klimaveränderung
S. Hoffmann, O. Kornadt CIB Weimar, 01.06.2006
WEIMAR: Minimum temperature HadRM3
spring summer winterautumn-15
-10
-5
0
5
10
15
tem
pera
ture
[°C
]
1970 2020 2050 2080
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Anforderungen an Gebäude durch langfristige Klimaveränderung
S. Hoffmann, O. Kornadt CIB Weimar, 01.06.2006
MUNICH: Minimum temperature HadRM3
spring summer winterautumn-15
-10
-5
0
5
10
15
tem
pera
ture
[°C
]
1970 2020 2050 2080
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Anforderungen an Gebäude durch langfristige Klimaveränderung
S. Hoffmann, O. Kornadt CIB Weimar, 01.06.2006
WEIMAR: Global radiation HadRM3
spring summer winterautumn year0
50
100
150
200
250
300
glob
al ra
diat
ion
[W/m
²]
1970 2020 2050 2080
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Phys
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Anforderungen an Gebäude durch langfristige Klimaveränderung
S. Hoffmann, O. Kornadt CIB Weimar, 01.06.2006
MUNICH: Global radiation HadRM3
yearautumn wintersummerspring0
50
100
150
200
250
300
glob
al ra
diat
ion
[W/m
²]
1970 2020 2050 2080
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Phys
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Anforderungen an Gebäude durch langfristige Klimaveränderung
S. Hoffmann, O. Kornadt CIB Weimar, 01.06.2006
Comparison of HadRM3 TRYs for the future:
- Big differences in hourly dry bulb temperature for Munich and Weimar - ?
- Problems of Ranking:- Day - 1 typical year (12 months) for 1970
and 1 average year for 2080- Scaling factor interpolates values for 2020 (0.27)
and for 2050 (0.57)- Reordering daily values for 2020 and 2050 like in 1970
or like in 2080
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Phys
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Anforderungen an Gebäude durch langfristige Klimaveränderung
S. Hoffmann, O. Kornadt CIB Weimar, 01.06.2006
1970 2020 2050 2080
8
10
12
14
16
18
20
22
tem
pera
ture
[°C
]
0
25
50
75
100
125
150
175
glob
al ra
diat
ion
[W/m
²]
Weimar Munich
Climate Change Weimar ↔ Munich
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Phys
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Anforderungen an Gebäude durch langfristige Klimaveränderung
S. Hoffmann, O. Kornadt CIB Weimar, 01.06.2006
1970 2020 2050 2080
-20
-10
0
10
20
30
40
50
tem
pera
ture
[°C
]
Weimar Munich
Climate Change Weimar ↔ Munich
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Anforderungen an Gebäude durch langfristige Klimaveränderung
S. Hoffmann, O. Kornadt CIB Weimar, 01.06.2006
Overheating hours during working hours (>25°C)
0
100
200
300
400
500
600
700
800
900
1970 2020 2050 2080
over
heat
ing
hour
s pe
r yea
r
DWD-TRY 4 Had RM 3 Weimar Had RM 3 Munich DWD-TRY 13
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Anforderungen an Gebäude durch langfristige Klimaveränderung
S. Hoffmann, O. Kornadt CIB Weimar, 01.06.2006
Overheating hours during working hours (>28°C)
0
100
200
300
400
500
600
700
1970 2020 2050 2080
over
heat
ing
hour
s pe
r yea
r
DWD-TRY 4 Had RM 3 Weimar Had RM 3 Munich DWD-TRY 13
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Anforderungen an Gebäude durch langfristige Klimaveränderung
S. Hoffmann, O. Kornadt CIB Weimar, 01.06.2006
Global and regional climate models
Daniela Jacob
Max- Planck-Institute for Meteorology, Hamburg
Outline
Motivation
Global climate modelling
Regional climate modelling
Results for Europe
Dresden, August 2002Dresden, August 2002•• Century flood of Rhine and Century flood of Rhine and MoselleMoselle in December 1993in December 1993
•• Century flood again of Rhine and Century flood again of Rhine and MoselleMoselle in January 1995in January 1995
•• Century flood of the Century flood of the OderOder in July 1997in July 1997
•• Flood of the Danube and Lake Constance in May 1999Flood of the Danube and Lake Constance in May 1999
•• Extensive and longExtensive and long--lasting floods in western Europe, inlasting floods in western Europe, inparticular south England and Wales, in the autumn of 2000particular south England and Wales, in the autumn of 2000
•• Flood of the Flood of the VistulaVistula in July 2001 in July 2001
•• Flood of the Danube in August 2002Flood of the Danube in August 2002
•• Century flood of the Elbe in August 2002Century flood of the Elbe in August 2002
•• Extreme precipitation and dreadful floods in southernExtreme precipitation and dreadful floods in southernFrance in September 2002France in September 2002
•• Severe flooding in parts along German rivers in JanuarySevere flooding in parts along German rivers in January20032003
From: Spiegel Nr. 7 2003; Quarterly report of the DWD, special tFrom: Spiegel Nr. 7 2003; Quarterly report of the DWD, special topic July 2003opic July 2003
Summer 2003: extreme Summer 2003: extreme droughtdrought in Europein Europe
Deviation of daily mean temperature 2003 from long term mean (1876-2000 ) in Karlsruhe
From: From: NasaNasa Goddard Institute for Space Studies; Inst. f. Goddard Institute for Space Studies; Inst. f. MeteorologieMeteorologie und und KlimaforschungKlimaforschung, Univ. , Univ. KarlsruheKarlsruhe
Into the future….
the IPCC process
results from global modelling
The IPCC
The Intergovernmental Panel on ClimateChange (IPCC) was set up in 1988 under thejoint auspices the United NationsEnvironment Program (UNEP) and the World Meteorological Organisation (WMO) to analyse the potential effect of human activities on climate .
IPCC Senarien
Global Air Temperaturechange
Annual meantemperaturechange
[° C]
A1B
B1
1 2 3 5 100
2071-2100 to 1961-90, Movie by M. Böttinger
What do the MPI-M Models project for the End of the 21st Century?
A general warming of the Earth of 2.5 to 4.0 °C, depending on the adopted scenario for CO2 increase, but with large regional differences:
In Europe: an increase of 3-4 °C (scenario A1B) or 2-3 °C (scenario B1)
An ice-free summertime Arctic ocean after year 2090
What do the MPI-M Models project for the End of the 21st Century?A more active hydrological cycle (precipitation), but
with large regional differences:
High Northern latitudes: warmer and wetter in winterSouthern Europe, Southern Australia, and South
Africa: dryer all yearAmazon, India and East Monsoon region: dryer in
the dry season, wetter in the wet seasonCentral Africa: wetter
Europe: reduction of 10-50% in precipitation, especially in the
Mediterranean, but wetter in Scandinavia.
~ 250 km
The Baltic Sea
~ 100 km
~ 50 km
~ 18 km
Total annual PrecipitationREMO 1/2 ° (1979-93) Observations (1971-90)
REMO 1/6 ° (1979-88) REMO 0.088 ° (1979-86)
days
per
dec
ade
Calculated trend in the number of hot days
(1960 to 2000) using ERA40-REMO results on 20 km grid
Hot day: 5K above mean of daily maximumSFB 512
Into the future….
results from the Prudence project
2071-2100: A2 (P-E=Runoff) changes
-30.0%
-25.0%
-20.0%
-15.0%
-10.0%
-5.0%
0.0%
5.0%
10.0%
15.0%
20.0%
Baltic Sea cat. Danube Elbe Rhine
Cha
nge
in P
-E
MPIDMIGKSSKNMICNRMETHHCSMHIUCMITCPMeanHadAM3H
Into the future….
SRES B2 REMO on 20 km resolution
chan
ge in
freq
uenc
y[p
erio
ds p
er 3
0 ye
ars]
length of period [days]
Frequency of summer day periods(Tmax > 25°C)
LuleaelvenLuleaelven
RheinRhein
EbroEbro
Summer day periods, REMO 20 km, SRES B2, 2071-2100 compared with 1961-1990
Numbers: Blue - today, red - future
Niedrigwasserperioden (Q < 750 m2/s) Pegel Kaub
0
2
4
6
8
10
12
14
3 bis 7 8 bis 14 15 bis 21 > 21
Periodenlänge (in Tagen)
Anza
hl
Nied
rigw
asse
rper
iode
n 1961-19902021-2050
Period length (days)
Low flow periods (Q < 750 m²/s)
Gauging station Kaub
Num
ber
of lo
w fl
ow p
erio
ds
Low flow periods in the Rhine river
Number of Wet days (>20 mm/day), B2
Winter
Summer
1961-1990 (2021-2050) - (1961-1990)
Thank you!
Dresden (Zwinger), August 2002
Hadley model climate change data and obtaining hourly values from the
daily statistics
Geoff LevermoreProfessor of the Built Environment
Dr David Chow, Manchester University & Tyndall
Climate Change Research Centre
BBuiltuiltEEnvironmentnvironmentRResearchesearchGGrouproup
OBSERVATIONS OF GLOBAL TEMPERATUREAnnual averages and long-term trends 1856-2000
Cha
nge
in te
mpe
ratu
re (°
C)
1860 1880 1900 1920 1940 1960 1980 2000
1.0
0.8
0.6
0.4
0.2
0.0
–0.2
Clim
atic Research U
nit: Jones et al. 1999
Cha
nge
in te
mpe
ratu
re (°
C)
Source: Hadley Centre, The Met.Office
Variations in Earth’s surface temperature 1000 to 2100
HadCM3• HadCM3 is a coupled Atmosphere-Ocean
General Circulation Model (AOGCM).• Unlike earlier AOGCMs HadCM3 does not need
flux adjustment (additional "artificial" heat and freshwater fluxes at the ocean surface) to produce a good simulation.
• The higher ocean resolution of HadCM3 is a major factor in this.
• HadCM3 has been run for over a thousand years, showing little drift in its surface climate.
How can we get climate-changeinformation on a smaller scale?
• Interpolating between GCM grid points adds no useful information and can be misleading.
• Adding future climate, coarse-scale changes from a GCM to high-resolution observations will not containdetailed prediction of future climate.
Statistical Downscaling
This technique uses observationsin today’s climate to derive relationships
between large-scale climate variables (e.g. surface pressure and atmospheric
temperature), and the surface
HadCM3
• HadCM3 has 19 atmospheric levels.• Horizontal resolution of 2.5° of latitude by
3.75° of longitude, (global grid of 96 x 73 cells).
• Resolution of about 417 km x 278 km at the Equator,
• 295 km x 278 km at 45° of latitude
Ensembles• Lorenz 1963; impossible to definitively predict the state of the atmosphere
more than approximately 10 days in advance, (chaotic process).
• Also, existing observation networks have limited spatial and temporal resolution, especially over the Pacific Ocean, gives uncertainty to initial state of the atmosphere; bad for numerical prediction.
• To combat use stochastic or "ensemble" forecasting. Numerous forecasts with different model systems, different physical parameterizations, or varying initial conditions.
• Ensemble forecast evaluated by the ensemble mean and spread of aforecast variable; represents the degree of agreement between ensemble members.
• Ensemble members in high agreement; forecast confidence high.
• Ensemble members diverge; poor forecast confidence.
Regional climate models (RCMs)• Local climate change influenced greatly by mountains,
etc (not well represented in coarse resolution, global models).
• Models of higher resolution cannot practically be used for global simulation of long periods of time.
• Hence RCMs, with a higher resolution (typically 50 km) constructed for limited areas and run for shorter periods (20 years or so).
• RCMs take their input at their boundaries and for sea-surface conditions from the global AOGCMs. Hadley RCMs for: Europe, the Indian subcontinent and southern Africa.
• Hadley RCM (run on a PC) for any region: PRECIS.
Regional Climate Model (RCM)• Resolution typically 50 km (300 km in a GCM).• Covers typically 5,000 km x 5,000 km; (roughly
the size of a box around Australia). • It is a comprehensive physical model.• Usually includes atmosphere and land surface,
but not generally an ocean component; (more complex).
• RCM, at its boundaries, is driven by atmospheric winds, temperatures and humidity output from a GCM.
RCM
horizontal resolution of 50km, 19 levels in the atmosphere (from the surface to 30 km in the stratosphere) and four levels in the soil.
Scenarios“New
technologies, materials and construction
processes are adopted and the
UK becomes more open to
non-traditionalbuilding
techniques.” “Improving the quality of
housing is a political priority for social as well as environmental reasons (energy
efficiency). However, efforts
are limited by budget
constraints”
Consumerism
Community
AutonomyInterdependence
World Markets(High)
Provincial Enterprise(Medium High)
Global Sustainability(Low)
Local Stewardship(Medium Low)
ConventionalDevelopment
Dry Bulb Temperature Comparisons
Heathrow 1-Day Comparison (1976-90)
-4.00
-3.00
-2.00
-1.00
0.00
1.00
2.00
99.6% 99.0% 98.0% 50.0% 2.0% 1.0% 0.4%Percentiles
Diff
eren
ce fr
om o
bser
ved
valu
es
HadCM3A2 (1 Day)
HadCM3B2 (1 Day)
HadRM3A2 (1 Day)
HadRM3B2 (1 Day)
Solar Irradiation Comparisons
Heathrow 1-Day Comparison (1976-90)
-20.00
-10.00
0.00
10.00
20.00
30.00
40.00
50.00
99.6% 99.0% 98.0% 50.0% 2.0% 1.0% 0.4%Percentiles
Diff
eren
ce fr
om o
bser
ved
valu
es
HadCM3A2 (1 Day)
HadCM3B2 (1 Day)
HadRM3A2 (1 Day)
HadRM3B2 (1 Day)
Errors using (Tmax+Tmin)/2 instead of mean
Sinusoidal Fit for known Tmax and Tmin
10.00
12.00
14.00
16.00
18.00
20.00
22.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
Tem
pera
ture
(deg
C
Known Tmax value
Known Tmin value
Month TMAX time (tmax) TMIN time (tmin) January 14 6 February 14 6 March 14 5 April 15 5 May 15 4 June 16 4 July 15 4
August 15 5 September 15 5
October 14 6 November 14 6 December 14 7
T M I N
T M A X
tm axtm in
3
9
15
21
2 5 8 11 14
(max + min)/2 =15
λmaxλmin
Mean = 17.4
Mean = 13.0
Cumulative hourly for YEARS
Yearly C um ulative Error Analysis of H ourly Tem perature Algorithm s for H eathrow 1976-1995
0%
5%
10%
15%
20%
25%
30%
35%
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
Year
Ave
rag
e E
rr
CIBSE Individua lDays
14R-1 Individua lDays
CIBSE link ing days
14R-1 link ing days
Q -S in
tmax = 14-00 and tmin = sunrise(R) - 1
Hourly for YEARS
Y early Average H ourly E rror Analysis of H ourly Tem perature A lgorithm s for H eathrow 1976-1995
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
Year
Ave
rag
e E
rr
CIBSE IndividualDays
14R-1 IndividualDays
CIBSE link ing days
14R-1 linking days
Q -S in
tmax = 14-00 and tmin = sunrise(R) - 1
Cumulative hourly for months
M onthly C um ulative Error Analysis o f H ourly Tem perature Algorithm s for H eathrow 1976-1995
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
14.00%
16.00%
18.00%
20.00%
1 2 3 4 5 6 7 8 9 10 11 12
M onth
Ave
rag
e E
rr
CIBSE IndividualDays
14R-1 IndividualDays
CIBSE link ing days
14R-1 linking days
Q -S in
tmax = 14-00 and tmin = sunrise(R) - 1
Hourly for months data
M onthly Average H ourly Error Analysis of H ourly Tem perature Algorithm s for H eathrow 1976-1995
0.00
0.10
0.20
0.30
0.40
0.50
0.60
1 2 3 4 5 6 7 8 9 10 11 12
M onth
Ave
rag
e E
rr
CIBSE IndividualDays
14R-1 IndividualDays
CIBSE link ing days
14R-1 linking days
Q -S in
tmax = 14-00 and tmin = sunrise(R) - 1
Q-Sin + CIBSE Tmax
Monthly Average Hourly Error Analysis of Hourly Temperature Algorithms for Heathrow 1976-1995
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1 2 3 4 5 6 7 8 9 10 11 12
Month
Ave
rage
Err
or
CIBSE IndividualDays
14R-1 Individual Days
CIBSE linking days
14R-1 linking days
Q-Sin (14R-1)
Q-Sin (CIBSETmaxR-1)
tmax = 14-00 and tmin = sunrise(R) – 1 But note new Q-Sin (CIBSE tmax, R-1)
Cumulative results Monthly Cumulative Error Analysis of Hourly Temperature Algorithms for
Heathrow 1976-1995
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
1 2 3 4 5 6 7 8 9 10 11 12
Month
Ave
rage
Err
or
CIBSE IndividualDays
14R-1 Individual Days
CIBSE linking days
14R-1 linking days
Q-Sin (14R-1)
Q-Sin (CIBSETmaxR-1)
Heathrow 1-Day 1% Exceedence (HadRM3)
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
Tem
p (d
eg C
) 1970s2020s (A2 & B2)2050s (B2)2050s (A2)2080s (B2)2080s (A2)
Heathrow 1-Day 99% Exceedence (HadRM3)
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
Tem
p (d
eg C
) 1970s2020s (A2 & B2)2050s (B2)2050s (A2)2080s (B2)2080s (A2)
Simulation
Hourly simulation of a building and its plant on a PC for a year.
Test Reference Year or
A near-extreme Design Year
Selection of TRYs
Cumulative Selection of TRY
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
-5 -3 -1 1 3 5 7 9 11 13 15
Temperature
Perc
enta
ge
20years
Year A
Year B
Year C
CIBSE Natural ventilation criterion
During the occupied hours of the year the dry resultant temperature should not exceed 25C (77°F) for more than
5% of the occupied year28C part is shown to be redundant
Fig. 2 London ranked summer average dry bulb temperature
12
12.5
13
13.5
14
14.5
15
15.5
16
16.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 2077 86 78 79 88 85 80 81 87 91 93 84 94 82 83 92 90 89 95 76
Dry
bulb
tem
pera
ture
Selection of Near-Extreme Summers
Example: Near Extreme Summer (1976 – 1995)
Sample office
5m
5m
10m12m
4m
4m
4m
3m
3m
3m
9m
Comparison of Models for TRY 1985 (Heathrow)
0
500
1000
1500
2000
2500
3000
3500
400019
65 L
ight
1965
Med
1965
Hea
vy
1976
Lig
ht
1976
Med
1976
Hea
vy
1985
Lig
ht
1985
Med
1985
Hea
vy
1995
Lig
ht
1995
Med
1995
Hea
vy
2002
Lig
ht
2002
Med
2002
Hea
vy
Htg
/ C
lg D
eman
d (k
Wh)
Real 1985 Heating
Real 1985 Cooling
HadRM3 1970sHeating
HadRM3 1970sCooling
HadCM3 1985Heating
HadCM3 1985Cooling
Conclusion
• Have hourly data from Hadley climate model daily data.
• Future work:– Compare different climate model data– Compare same buildings in different locations
for current and future data.
ingenieurbürofür bauklimatikhausladen + meyer
Thermal Comfort in a changing Climate
Dr.-Ing. Christoph MeyerIngenieurbüro für Bauklimatik / Consultancy for Building ClimatologySickingenstr. 10D-34117 [email protected]
ingenieurbürofür bauklimatikhausladen + meyer
Thermal Comfort according to Fanger / ISO 7730
§ Based on the requirement of an even human body‘s heat balance.
§ Classification and evaluation properties of indoor climate:
• Operative temperature.- Air temperature.- Radiant temperature.- Air velocity.
• PMV: Predicted Mean Vote.
• PPD: Predicted Percentage of Dissatisfied.
ingenieurbürofür bauklimatikhausladen + meyer
PMV and PPD according to Fanger / ISO 7730
0%
20%
40%
60%
80%
100%
-3 -2 -1 0 1 2 3PMV
PP
D
very cold very hot
ingenieurbürofür bauklimatikhausladen + meyer
Limitations and Drawbacks of ISO 7730
§ Evaluation based upon experiments in HVAC-controlled test chambers.
§ Valid only under stationary conditions.
§ Not suitable for naturally ventilated buildings.
§ Outdoor conditions not taken into account.
ingenieurbürofür bauklimatikhausladen + meyer
Predicted and ascertained Vote in HVAC-controlled Buildings
indo
orop
erat
ive
tem
pera
ture
[°C
]
mean monthly outdoor air temperature [°C]
ingenieurbürofür bauklimatikhausladen + meyer
Predicted and ascertained Vote in naturally ventilated Buildings
indo
orop
erat
ive
tem
pera
ture
[°C
]
mean monthly outdoor air temperature [°C]
ingenieurbürofür bauklimatikhausladen + meyer
Comfort Temperature in naturally ventilated Buildings according to ASHRAE 55
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Climatic Parameters affecting thermal Building Behaviour in hot Summer Conditions
§ Ambient temperature:• Heat gains predominantly through day-time ventilation.• Possible heat losses through night-time ventilation.
§ Solar irradiation:• Penetration through transparent facade components.• Absorption on opaque components.
§ Humidity:• Limits the capacity of cooling components• or causes need for dehumidification.
§ Wind• Supports natural ventilation.• Might cause unwanted air exchange.• Limits the usability of external shading devices.
ingenieurbürofür bauklimatikhausladen + meyer
Thermal Comfort under the predicted climatic Changes
Winter
§ Improved thermal comfort conditions.§ Reduced heating energy requirements.
Summer
§ HVAC-controlled buildings:• No change in occupants‘ perception of thermal indoor conditions foreseeable.• Without adaption of buildings‘ designs, more cooling power will be required for maintaining
today‘s level of thermal comfort.
§ Naturally ventilated buildings:• Occupant‘s adaption will ease the effect of rising outdoor air temperature on thermal comfort.• Degree of necessary building design‘s adaption yet uncertain.
Test Reference Years and Design Summer Years for the UK; selection
and quality assurance
John Parkinsonand Geoff Levermore
Manchester University, UK.
Weimar June 1st 2006
BBuiltuiltEEnvironmentnvironmentRResearchesearchGGrouproup
Object: to provide up-to-date weather data for the UK Building Industry.
History: Existing guide provided hourly data for 3 sites only for the 20 years 1975-1995.
Since then there have been several warm summers. The guide needed to be updated to years 1985-2005 and also extended to more sites.
14 sites in all were chosen for which data is available.
• TOWN WEATHER STATION Identification Latitude Longitude Elevation(m)
• BELFAST Aldergrove 3917 54.663 -6.222 63• BIRMINGHAM Coleshill 3535 52.48 -1.689 96• BIRMINGHAM Elmdon 3534 52.452 -1.741 96• CARDIFF Rhoose 3715 51.4 -3.343 65• CARDIFF StAthan 3716 51.404 -3.441 49• EDINBURGH Turnhouse 3160 55.951 -3.347 35• GLASGOW Abbotsinch 3140 55.869 -4.431 5• LEEDS Leeds.w.c. 3347 53.8 -1.56 64• LONDON Heathrow 3772 51.479 -0.449 25• MANCHESTER Ringway 3334 53.356 -2.279 69• NEWCASTLE Newcastle.w.c. 3245/6 54.977 -1.597 52• NORWICH Coltishall 3495 52.756 1.356 17• NOTTINGHAM Nottingham.w.c. 3354 53.005 -1.25 117
(also called Watnall) • PLYMOUTH Plymouth.w.c. 3827 50.354 -4.121 50
(also called Mountbatten) • SOUTHAMPTON Southampton.w.c. 3865 50.898 -1.408 3
(also called Mayflower Park) • SWINDON Boscombe Down 3746 51.161 -1.754 126
• Table 1 Coordinates of 14 UK towns (16 sites) selected, with Latitude, • Longitude and Elevation of weather stations.
XX
XX
XX
X
X
X X
X
X
X
X
Approximate locations of 14 sites selected for weather data processing
Data required for the guide is hourly values of the following:
PWC (Present Weather Condition –- code) Cloud (Cloud cover -- 1/8ths)DryT (Dry bulb temperature -- degC )WetT (Wet bulb temperature -- degC)Press (Pressure -- mb)WD (Wind direction – degrees)WS (Wind speed – knots)GlobalRad (Global radiation -- watts/sq. metre)DiffuseRad (Diffuse radiation – watts/sq. metre)
In fact radiation data is rarely recorded so it is necessary to synthesise this from the Cloud Cover data using an algorithm of Tariq Muneer (Napier University, Edinburgh).
Process is in two stages:
1. Download the measured data and sanitise it.
2. For each site construct:
a. the Test Reference Year (TRY)
b. the Design Summer Year (DSY)
3. Quality checks conducted.
Download and sanitise data.
Hourly weather data via BADC (British Atmospheric Data Centre) from the UK Meteorological Office.
Data is incomplete or unsuitable the following main ways:
1. Gaps of one or more hours. Either whole hour is missing or some of the data is not recorded (e.g. Dry bulb T).
2. Duplicate entries are present for an hour due to data being collected from more than one source at the same site. Usually this is from a manual record (called SYNOP) and an automated record (called METAR).
In addition there was a change in the data format on 2000, so files before and after this date have to be processed separately.
The sanitisation process consists of first restoring missing hours with blank records and removing duplicate entries.
Then missing data is interpolated provided the gaps in the data are less than 108 hours (i.e. approximately 15% of a month).
Most gaps were far less than this, typically 1-5 hours.
Interpolation is carried out in two main ways:
1. Linear interpolation for wind speed and direction.
2. Cubic spline interpolation for all other variables.
14.214.414.614.8
1515.215.415.615.8
1616.2
10 12 14 16 18 20 22
Time (h)
Tem
pera
ture
(C)
cubic splinelinear interpolationactual data
Comparison of linear and cubic splineinterpolation between points A and B.
Test Reference Year (TRY)
Composite year formed by taking the most ‘typical’ January of the 20, then the most ‘typical’ February etc and combining to form a single year.
Most typical January is not same as the most averageJanuary.
To select the most typical January we use the Finkelstein-Schafer method.
Make a Cumulative Distribution Function (CDF) for all 20 Januaries.
Look at CDF for each separate January. Find the one which is closest to the overall CDF.
We do this using daily averages for each of the 3 variables:
Cloud cover (Cloud) ,
Dry bulb temperature (DryT)
Wind Speed (WS)
CDFs of different gaussian data
0.0
0.2
0.4
0.6
0.8
1.0
-3 -1 1 3
Standard deviation
Cum
ulat
ive
frequ
ency
0,11,10,20.3,1
Illustration of FS method using Gaussian CDFs. (0,1), i.e. mean = 0, s.d. = 1, is the overall (target) CDF. Distribution (0,2) has same mean as (0,1) but (0.3,1) is more ‘typical’ since it is closer to (0,1) on balance.
0
0.2
0.4
0.6
0.8
1
-5 0 5 10 15
Dry bulb temperature (C)
Cum
ulat
ive
prob
abili
ty
All januariesJan-96Jan-00
CDFs of DryT for two different Januaries for London, Heathrow, compared to overall.Clearly, 1996 is the best.
Selection of TRYs
Cumulative Selection of TRY
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
-5 -3 -1 1 3 5 7 9 11 13 15
Temperature
Perc
enta
ge
20years
Year A
Year B
Year C
Month Year selected
January 1988February 2004March 2004April 1992May 2000June 2001July 1991
August 1996September 1987
October 1988November 1992December 2003
Selected years for each month for the TRY for Heathrow with the start year 1983 and the end year 2004. Selection using FS method with equal weighting for Cloud, DryT and WS.
0123456789
10
1980 1985 1990 1995 2000 2005
Year
Val
ue o
f sta
tistic
FSmeanmean all jans
FS statistics for different years for dry bulb temperature for London, Heathrow.
1.5
2
2.5
3
3.5
4
4.5
1980 1985 1990 1995 2000 2005
Year
Valu
e of
sta
ndar
d de
viat
ion
st devst dev all jans
Comparison of the standard deviations for the days in the months for London, Heathrow (1993 is indicated as closest tothe all Januaries average).
Smoothing
When combining months from different years to form the TRY it is necessary to smooth at the change over.
This is done by a simple linear interpolation replacing the last two hourly values of the previous month and the first two of the next month.
0%
5%
10%
15%
20%
25%
30%
35%
0 0.5 -0.5 1 -1 1.5 -1.5
Change in dry bulb temperature (K)
Belfast (Aldergrove) temperature changes from one hour to the next
-4
-2
0
2
4
6
8
1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900
hour
Dry
bul
b te
mpe
ratu
re (C
)
Actual data
Old method
New method
Smoothing methods for dry bulb temperature for adjacent months (February, March) in the TRY for Belfast, Aldergrove.
1014
1016
1018
1020
1022
1024
1026
1028
1030
1032
1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900
hour
Pres
sure
(hPa
)
Actual data
old method
new linearmethod
Smoothing methods for atmospheric pressure for adjacent months (February, March) in the TRY for Belfast, Aldergrove.
Design Summer Year (DSY)
•This is a single actual year selected to be the one which has the third highest average summer temperature. Only DryT is used in the selection.
•Again use daily averages to find the overall average temperature for the 6 months April – September.
•Some sites data for whole months is missing. If not a summer month then that year can still be used for choosing DSY.
•But if the third highest summer has other months missing then the fourth highest must be used instead.
Fig. 2 London ranked summer average dry bulb temperature
12
12.5
13
13.5
14
14.5
15
15.5
16
16.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 2077 86 78 79 88 85 80 81 87 91 93 84 94 82 83 92 90 89 95 76
Dry
bulb
tem
pera
ture
Selection of Design Summer Years (DSYs)
Example: Near Extreme Summer (1976 – 1995)
Quality Checks
Since the data is measured data there is a limit to how much checking can be performed.
In the next few slides we show some basic checks.
89
1011121314151617
Edinbu
rghBelf
ast
Glasgo
wNew
castl
eNott
ingha
mBirm
ingha
mSwind
onNorw
ichMan
ches
terCard
iffLe
eds
Plymou
th
Southa
mpton
Lond
on
Tem
pera
ture
(C)
DSY summer meanTRY summer meanDSY year meanTRY year mean
DSY > TRY as expected
8.08.59.09.5
10.010.511.011.512.0
49 51 53 55 57
Latitude (degrees)
TRY
mea
n te
mpe
ratu
re (C
)
London
Glasgow
Plymo uth
Swindon
TRY mean temperature variation with latitude (coefficient of determination [r2] = 0.733)
80
100
120
140
160
180
200
Glasgo
wEdin
burgh
Belfas
t
Newca
stle
Notting
ham
Manch
ester
Leed
sSwind
on
Southa
mpton
Birming
ham
Plymouth
Norwich
Lond
onCard
iff
Mea
n gl
obal
hor
onta
l so
lar i
rrad
ianc
e (W
/m2)
iz DSY gsi year meanTRY gsi year meanDSY gsi summer meanTRY gsi summer mean
Horizontal global solar irradiations can be close (selection only on temperature)
90
95
100
105
110
115
49 51 53 55 57
Latitude (degrees)
TRY
mea
n s
olar
hor
izon
tal
irrad
ianc
e (W
/m2)
CardiffNorwich
Nottingham
Solar horizontal irradiance does reduce with latitude (coefficient of determination (r2) is 0.802).
Conclusion, discussion
More TRYs, DSYs in UK.
TRYs used in other European and N American countries; same selection?
DSYs; do others use them?
EC collaborative project?
MÜLLER-BBM
-20.0
-15.0
-10.0
-5.0
0.0
5.0
10.0
15.0
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25.0
30.0
35.0
Weather data as input for thermal simulationsRelevance to building design process
Gunter Pültz, Müller-BBM, Department of Building Climatology
MÜLLER-BBM
Governmental building regulations in Germany:As parts of the building application:
Several certificates of building constructionsCertificate for saving energy (EnEV)
Verification of avoiding overheating in summeraccording to DIN 4108-2:2003-07
Simple procedure:only valid for simple rooms (small box rooms)Sophisticated, engineering-like procedure(= dynamic, zonal, thermal simulations):valid for all kinds of rooms (glazed atria, largehalls, wintergardens, double skin facade, etc....BUT ALSO valid for simple box rooms
Building regulations with requirements for roomtemperatures in summer – German standard DIN 4108-2
MÜLLER-BBM
Step1: Definition of a temperature limit (25...27°C) for eachplace in Germany (3 more or less sunny regions)
Building regulations with requirements for roomtemperatures in summer – German standard DIN 4108-2
e.g. a temperaturelimit of 26°C isassigned to Munich !
MÜLLER-BBM
Step 2: Limitation of overheating during summer:Exceeding the temperature limit is accepted only for10% of the „time of presence“ (= quotation fromDIN 4108-2) ! Example for an office building:If time of presence is referred to occupancy (workingtime) during a complete year, following is valid:40 h/w*52w/a ≈ 2100 h/a, therefrom 10% = 210 h/a
Building regulations with requirements for roomtemperatures in summer – German standard DIN 4108-2
Resulting summer design criteria for an office buildinglocated in Munich according to DIN 4108-2 (must be fulfilled):
Frequency of overheating hours per year (only while workingtime) can be determined only by zonal, thermal simulations ...
T > 26°C for max. 210 h/a
MÜLLER-BBM
Building regulations with requirements for roomtemperatures in summer – European standard prEN 15251
European Parliament and Council (2002/91/EG):Energy Performance of Buildings Directive (EPBD)
Put into national legislation in Germany (end of 2006)
New „Energiepass“(energy certificate)for every building
with a limitation of it´senergy consumption
Energy consumption
Evaluation of building´squality according to the new prEN 15251:Definition of criteriaand their limitations
Building Quality
MÜLLER-BBM
Building regulations with requirements for roomtemperatures in summer – European standard prEN 15251
prEN 15251, 2005: Criteria for the indoor environmentincluding thermal, indoor air quality, light and noise
Air-conditioned rooms: mechanical ventilation
and/or cooling plant
Thermal comfort NoiseLight Air quality
Temperatures limitsaccording to EN ISO 7730
Free ventilated roomswith natural ventilation
and no cooling plant
Temperatures limitsaccording to
prEN 15251, part 8
MÜLLER-BBM
Building regulations with requirements for roomtemperatures in summer – European standard prEN 15251
20
21
22
23
24
25
26
27
28
29
30
31
32
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Mittlere monatliche Außentemperatur [°C]
Inne
ntem
pera
tur [
°C]
Klasse A
Klasse B
Klasse C
German design weather July,
sunny region 3 (acc. VDI 2078)
prEN 15251, Addendum A: Acceptable indoor temperatures(temperature limitations) for free ventilated rooms (≈ 28...30°C), taking into account the human adaption to ambient heat
MÜLLER-BBM
Building regulations with requirements for roomtemperatures in summer – European standard prEN 15251
Long term evaluation (for rooms without a cooling plant):prEN 15251, Chapter 8.2.1.2.Acceptable overheating hours per year with excess of tempe-rature limit = max. 3% of occupancy resp. working time
The determination of maximum summer temperatures as well as overheating hours per year can either be measured orcalculated.For new buildings zonal, thermal simulations arerecommended (prEN 15251, Chapter 8.4.)
The use of simulations is conform to official standards !!!
MÜLLER-BBM
Relevance of simulation results to governmental buildingregulations and evaluation of building quality
Summer room temperatures (determined by thermal simulations)show following relevance to the building design process:
Official certificate for limitation of overheating(part of the building application)Ranking of the building´s thermal quality (classes A...C)as a base for it´s marketing
These infos are the base for the design decision, whether airconditioning/cooling is necessary for the building or not. Of coursethis decision shows an enormous impact on the budget !
These simulation results effect the building´s profitability !
Reliable and adequate weather data are needed as input datafor these simulations !!!
MÜLLER-BBM
Thermal simulations currently can base on following weatherdatasets of a complete year (8760 h/a):
Original test-reference-year (TRY) from the DeutscherWetterdienstes (DWD)
Test-reference-year (TRY) from the DWD with embeddedextrem summer period (created by the user, not by the DWD)
Dataset from the software METEONORM: Averaged Year
Dataset from the software METEONORM: Extreme Year
weatherdata from ASHRAE: IWEC-datasets for several citiesin Germany (i.e. Munich, Berlin, Frankfurt, Stuttgart, a.s.o...)
Currently available weather data for Germany
MÜLLER-BBMCurrently available weather data for Germany
most important parameters for room temperatures
Weather parameters with the most significant effecton the indoor room temperatures (only valid fortransparent facade components):
Global radiation = direct + diffusive radiationSolar heat gain by radiationtransmission and secondary heatflux (from glass pane warmed up byabsorption of solar radiation)
Ambient air temperatureConvective heat gain by air exchange between room and ambient air through openedwindows
Different fromenergy-calcu-lations !!!
MÜLLER-BBMCurrently available weather data for Germany
comparison of global radiation
Averaged global radiation varies during summer for up to 20% !
Global radiation - saison averaged
0
50
100
150
200
250
spring (01.03 - 31.05.) summer (01.06. - 30.08.) autumn (01.09. - 30.11.) winter (01.12. - 28.02.)
Glo
bal r
adia
tion
in [W
/m²]
300
MUC-2003MUC-Meteo-extTRY13-extremMUC-Meteo-avemunich_iwecTRY13
MÜLLER-BBMCurrently available weather data for Germany
comparison of ambient air temperaturesAmbient air temperature (day with maximum mean value)
10
15
20
25
30
35
40
01:00 03:00 05:00 07:00 09:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00
time of day (MET)
tem
pera
ture
in [°
C]
MUC-2003
MUC-Meteo-ext
TRY13-extrem
TRY13-city
MUC-Meteo-ave
munich_iwec
TRY13
Ambient air temperature varies in summer for up to 5°C !
MÜLLER-BBM
Significant differences of available datasets !
Currently no specification is available with an official instruc-tion, which datasets are to be used for the determination ofsummer temperatures (maxima as well as frequencies)
Thus planning teams are facing uncertainty concerning thenecessity of air-conditioning ...
Consultings/simulationists can predefine the must of an air-conditioning by selecting an appropriate weather dataset !?!?
More and more lawsuits are filed by tenants for obligingthe vendor/principal to retrofit air-conditioning ....
As retrofitting is very expensive the principal often accuses theplanning team for bad planning decisions ...
Currently available weather data for Germanysummary
MÜLLER-BBM
What is an urban heatisle ?
Weather data for Germanyconsideration of urban heat isles
Scan of surface temperatures on a hot, sunny day in Munich:
Huge temperaturedifferences bet-ween suburbs(with greens and water) and thecity-center !
MÜLLER-BBMWeather data for Germany
consideration of urban heat isles
Temperature peaks at specific locations in the city are the so called „urban heat isles“ ....These show an essential influence on indoor room temperature !
Scan of surface temperatures on a hot, sunny day in Munich (cut-out):
MÜLLER-BBMWeather data for Germany
consideration of urban heat islesFor thermal simultions not surface temperatures, but ambient air temperature is needed as input data.Results of a research project focusing on the urban climate in Munich (sponsered by the Bavarian Ministry of Environment):
Conclusions:Ambient air temperature isup to 4 K higher at urbanheat isles than at suburbsThe increase of ambient airtemperature depends on the ratio of sealed groundarea
Temperature increase in Munich
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Meteorological stations usually are located in the suburbs of acity for avoiding the influence of neighbouring buildings (orother thermal masses like streets, fabrics, a.s.o) on theirmeasurementsAs all types of datasets are basing on measured data from theweather stations, it is evident, that - up to now - urban heatisles are NOT considered in any available weather-dataset !Thus engineers are using a very, very coarse approach forconsidering urban heat isles in simulations for buildings:
Tamb, city-center = Tamb, original + 2 K (!)A more sophisticated model for considering the building´slocation somewhere in the city would be very helpfull !Such a model must be developed by urban-meteorologists;perhaps ENVI-MET is a possible candidate ... ?
Weather data for Germanyconsideration of urban heat isles
MÜLLER-BBM
All available weather datasets for Germany has beendeveloped by using measured data of the years 1961-1990(= reference weather periode of WMO) !The indisputable change of climate in the recent 15 years isNOT considered in the available weather datasets !
Weather data for Germanyfuture climate change
17.0
17.5
18.0
18.5
19.0
19.5
20.0
2003 1947 1994 1992 1983 2002 1911
mea
n da
y te
mpe
ratu
re [°
C]
Hottest summers in the last 100 years in Germany
Only ONE sum-mer of the refe-renceperiod isin the summerranking list, but>50% are fromthe last 15 years !
MÜLLER-BBMWeather data for Germany
future climate changeA trend of increasing temperatures must be constituted ....
... simulationists need weatherdata for future years !!!
MÜLLER-BBM
Effects of weatherdata on predicted room temperatureTypical office room in Germany
Simulation-model: Characteristics:No cooling plantNatural ventilation by openedwindowsExternal shading systemNight ventilation for heat-discharge during night
MÜLLER-BBM
Effects of weatherdata on predicted room temperatureOverheating in summer - frequency of inreased temperatures
800
0
100
200
300
400
500
600
700
> 26°C > 28°C > 30°C
over
heat
ing
hour
s pe
r yea
r [h/
a]
MUC-2020MUC-2003TRY13-extrem-cityTRY13-cityTRY13-extremMUC-Meteo-extTRY13munich_iwecMUC-Meteo-ave
?
?
?Official limit 4108-2
ERGO: The choice of weatherdata predefines the fullfillmentof the requirements according to official building regulations !!!
MÜLLER-BBM
Thermal simulations results show an enormous effect onbuilding´s energy efficiency, quality and profitabilityThe yearly weather-datasets, currently used for simulations ofbuildings, show following defects:
Inclusion of enough extrem summer periods not yetclarified (official instruction which dataset to be used)Temperature increase in the center of cities (urban heatisles) not yet included – missing a sophisticated modelClimate change of the last 15 years not yet consideredFuture climate change is NOT taken into account in building design process up to now !
Houses are built for the next 50...100 years, thus reliableweather-datasets are needed for future years
Weather data as input for thermal simulationsSummary
MÜLLER-BBM
Thank you for your attention !
Müller-BBM, Building ClimatologyGunter Pültz
MÜLLER-BBMEuropean standard EN ISO 7730:2005 –Ergonomics of the thermal environment
PMV – predicted mean vote
PP
D –
pred
icte
dpe
rcen
tage
of d
issa
tisfie
d
MÜLLER-BBM
Einteilung der PMV-/PPD-WerteDIN EN ISO 7730:2003 (Entwurf)
table A.1 – 3 categories of thermal environment
European standard EN ISO 7730:2005classifications for thermal comfort
MÜLLER-BBM
...depending on the room-/building type:
European standard EN ISO 7730:2005temperature limits for different quality classes
MÜLLER-BBM
Quotation of chapter 8: long term evaluation„If these criteria must be fulfilled for everytime, including extreme hot weather situations, the plant´s cooling power will be veryhigh. Consideration of ecomomic and environmental aspectsyields acceptable, limited time periods with excess of definedPMV/PPD borderlines.“
ERGO: Overheating hours per year can be defined freely in agreement with the principal !
European standard EN ISO 7730:2005free definition of overheating hours per year
MÜLLER-BBMAccepted indoor temperatures
results of recent scientific studies in several countries
Air-conditioned rooms Free ventilated rooms
Conclusions: The PMV-model predicts accepted indoor tempera-tures for air-conditioned rooms quite well, but failsfor free ventilated rooms !!!Thus these rooms need their own criteria ......
The Assessment of CO2-Emissions in the Design Phase Roman Rabenseifer1
1Department of Building Construction, Slovak University of Technology, Bratislava, Slovakia ABSTRACT: The paper under preparation will show the life cycle of a modern family house built as a low-energy building from the viewpoint of CO2-emissions. It will compare the energy savings in the course of its service life in relation to energy input necessary for its assembly and manufacturing of single building products (in both cases the fossil fuels based energy is traced only). The service life is described using standardized calculation methods for energy balance of buildings. The energy input data are based on information originating from building industry. The results will be discussed in the terms of:
− Whether the perception of buildings as power plants using renewable sources of energy is justified in relation to energy inputs and CO2-emissions,
− Whether the currently used energy demand calculations should not be complemented by information on energy inputs needed for building assembly and manufacturing,
− Whether alternative and lasting (sustainable) building materials would be a kind of solution, − Whether the energy savings by building industry in the course of manufacturing could lead to
other architectural solutions than so called “low-energy design”, and
− Whether it is possible in the design phase to consider the questions of CO2-emissions due to the life cycle of buildings at all?
Keywords: energy, CO2-emissions, life cycle of buildings, service life, assessment INTRODUCTION The European countries, the economy of which is based on export of industrial products and services and completely dependent on imports of fossil fuels, systematically support the improvement of energy efficiency of buildings. They do it in two basic ways:
− Normatively and legislatively, using restrictions in order to ensure the basic quality of buildings from the viewpoint of energy effectiveness, e.g. by requiring more and more improved and detailed investigation of the future energy demand for heating and hot water preparation and by suitable systems of criteria,
− Motivating, using various state and communal programs, usually the aim of which is the effective use of energy from fossil fuels and the development of alternative and ecological energy sources (solar radiation, water, wind).
These two basic instruments focus almost entirely on building performance after its assembly on the building site. Explained in terms of the life cycle of a building, the mentioned policy does not take into consideration the energy needed either for production of the building materials or for assembly of a building or for its dismantling. The main argument for this exclusive concentration on the service life of a building is that 40% of the total energy consumption is caused by operation of the buildings. The remaining 60% fall on industry and transportation, whereas 20% out these 60% are supposed to be caused by production of building materials, building processes, renovation and dismantling of the buildings. The following case study wants to show that this argumentation is no longer valid for buildings built in compliance with existing standards or even in a low-energy way. As the energy supply needed for the operation of such buildings is quite low, a significant rise of the building industry portion within this imaginary scheme should be a consequence. In this context several questions occur, e.g. whether the so-called low-energy design is justified in relation to energy inputs and CO2-emissions. PROBLEMS
The crucial problem of the presented comparison of CO2-emissions due to the expected building operation on the one side and due to the built-in energy on the other side was the way of gathering at least a little reliable data regarding the CO2-emissions due to the production of building materials. Usually, the building industry does not record information on kilograms or tons of CO2-emissions per building product, e.g. brick or window. Under circumstances these values could be derived from the annual reports of single companies, if they would have been at our disposal and would have included the CO2-emissions and the number of products per year. Unfortunately this was not the case. Therefore, some research in the libraries and on the internet was necessary. This effort yielded two works that might be a serious source of information. The first one was the MIPS concept developed by Professor F. Schmidt-Bleek and the theory of MIPS calculation elaborated by M. Ritthoff, H. Rohn and Ch. Liedtke from Wuppertal Institute for Climate, Environment and Energy. The notion MIPS stands for Material Input Pro Service Unit and represents an indicator of the precautionary protection of the environment. The second source was the GEMIS software (Global Emission Model for Integrated Systems) developed by the ECO-Institute. In this paper particularly the use of process based CO2-emissions calculated by GEMIS was made. The calculated energy demand of the case-study building was converted into CO2-emissions using the conversion table published in “Der österreichische Gebäude-Energieausweis – Energiepassport” written by Professor Panzhauser et all. Of course, only the fossil-fuels-based CO2-emissions were traced. CASE STUDY The construction of family houses (up to 120 m2) and apartment buildings (having flats with up to 80 m2) is in Slovakia often supported by the State Fund of Housing Development. The basic conditions are the minimum age of 18 years, the regular income of the applicant, the planning permission, which implies the fulfillment of the Slovak building standards, and a detailed and neutral assessment of the future construction costs. The latter is a base for calculation of the amount of the state mortgage that offers considerably lower interests than commercial banks. In the presented case the assessed future construction cost are in a range of approximately 80.000,- €. The figs. 1, 2, 3 and 4 show the floor plans, cross section and the elevations of the family house in consideration. The GEMIS software indicates under the item building construction the equivalent CO2-emissions per monetary unit as 0,46708 kg CO2 / €. This corresponds to 37.366,-kg of equivalent CO2-emissions due to the production of the building materials and the assembly of the case-study family house (built-in energy). The Fig. 5 compares these built-in energy based CO2-emissions with the ones based on the expected building operation (energy demand for heating and warm water preparation) in relation to the building service life. In addition to this, the CO2-emissions due to the production of some building materials are introduced in the table 1 (source: GEMIS software). The Fig. 6 shows the position of the Slovak heat demand requirements converted into CO2-emissions within the classification of the CO2-emissions due to heating and warm water preparation described in “Der österreichische Gebäude-Energieausweis – Energiepassport”. The black cross indicates the position of the investigated family house.
Fig. 1. Floor plans of the investigated family house
Fig. 2. Front elevations of the investigated family house
Fig. 3. Lateral elevations of the investigated family house
Material / Unit CO2-emissions
[kg] Bricks [kg] 0,93 PUR Hard-foam [kg] 3,67 PVC Window-frame (manufacture) 2,37
Tab. 1. The CO2-emissions due to
the production of some building materials
Fig. 4. Cross-section
Fig. 5. Comparison of the built-in energy based CO2-emissions with the ones based on the expected building operation in relation to the building service life
CONCLUSIONS It is obvious that the initial (built-in) energy needed for the assembly of building and its manufacturing is inadequate in comparison with the energy needed for the building operation. The current exclusive focusing on the energy efficiency of the building operation leads to heavy insulated building envelopes and to the use of alternative energy sources on a decentralized basis. The family houses often turn to small power plants even selling the surplus energy to public grids. One might claim that this superfluous energy over the time possibly equalizes the initial CO2 intensiveness of the building assembly and manufacturing. However, the CO2-emissions are already in the atmosphere and this process is irreversible. If except of the energy efficiency also the reduction of the CO2-emissions is our common goal, then the imaginary triangle “initial emissions – quality of the building envelope – building operation” should be shifted from asymmetric form towards more symmetric one in favour of the reduction of the initial CO2-emissions.
0
5
10
15
20
25
30
35
40
45
1 5 9 13 17 21 25
Time in years
CO
2-em
issi
ons
[t C
O2/y
ear]
CO2-Emissions due toheating and warmwater
CO2-Emisssionscaused by buildingconstruction(manufacturing andassembly)
Fig. 6. Slovak heat demand requirements converted into CO2-emissions within the classification of
the CO2-emissions due to heating and warm water preparation described in [3]. The black cross indicates the position of the investigated family house. Its characteristic length is 1,43 m and the amount of CO2-emissions slightly above 15 kg/(m2.year).
In order to achieve this a detailed methodology for recording the CO2-emissions due to the building assembly and manufacturing should be developed. While we are able to assess the future CO2-emissions caused by building operation, e.g. the Dutch standard NEN 5128 (2004) offers an informative annex regarding the calculation of CO2-emissions, a reliable methodology for recording the initial CO2-emissions is still missing. A good attitude might be the MIPS methodology described in the work of M. Ritthoff, H. Rohn and Ch. Liedtke and applied on building industry. In addition to this, in the course of the planning permission process or at least in case of buildings subsidized by the state respective certificates from the building industry regarding the quality of its products, e.g. kg of CO2-emissions per unit of produced material, should be required, as well as calculation of the overall CO2-emissions. According to the opinion of the author this would represent a system approach that would force the building industry to look more intensively for clean energy solutions that would reduce the CO2-emissions. Perhaps, as a consequence, a new architectural style based on less insulated buildings supplied from central green power plants could originate. ACKNOWLEDGEMENTS This work was supported by the Slovak Science and Technology Assistance Agency under the contract No. APVT-20-042202. PUBLICATIONS Ritthoff, M., Rohn, H., Liedtke & Ch., Merten, T. 2002, MIPS Berechnen. Ressourcenproduktivität von Produkten und Dienstleistungen, Wuppertal Institut für Klima, Umwelt und Energie, GmbH, im Wissenschaftszentrum Nordrhein-Westfallen (in German) Schmidt-Bleek, F. 2000, Das MPIS Konzept: weniger Naturverbrauch – mehr Lebensqualität durch Faktor 10, Munich: Knaur (in German)
Fantl, K., Panzhauser & E., Wunderer, E. 1996, Der österreichische Gebäude – Energieausweis. Energiepass, TU Wien, (in German) GEMIS software (Global Emission Model for Integrated Systems) developed by Eco-Institute, Institute for Applied Ecology, and available at http://www.oeko.de/service/gemis/de/index.htm Verordnung über energiesparenden Wärmeschutz und energiesparende Anlagen-technik vom 16.11.2001 (EnEV) (in German) Österreichische Norm ÖN B 8110-6: Wärmeschutz im Hochbau. Grundlagen und Nachweisverfahren. (1.12.2004) (in German) Nederlandse norm NEN 5128-2004 (nl), Energieprestatie van woonfuncties en woongebouwen – Bepalingsmethode (Energy performance of residential functions and residential buildings - Determination method) (in Dutch) STN 730540 Tepelnotechnické vlastnosti budov – Tepelná ochrana budov – Časť 2: Funkčné požiadavky (Thermal and technical properties of buildings – Thermal protection of buildings – Part 2: Functional requirements) (in Slovak) STN 730540 Tepelnotechnické vlastnosti budov – Tepelná ochrana budov – Časť 4: Výpočtové metódy (Thermal and technical properties of buildings – Thermal protection of buildings – Part 4: Calculation methods) (in Slovak)
International Council for Research and Innovation in Building and Construction
CIB’s mission is to serve its members through encourag-ing and facilitating international cooperation and information exchange in building and construction research and innova-tion. CIB is engaged in the scientific, technical, economic and social domains related to building and construction, supporting improvements in the building process and the performance of the built environment.
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International Council for Research and Innovation in Building and Construction
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