w108 - climate change and the built environmentsite.cibworld.nl/dl/publications/w108.pdf ·...

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


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


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!


About DKRZ (3): Data Archive 1992About DKRZ (3): Data Archive 1992--20042004

[ TB ]


Complexity of the Climate System (1)Complexity of the Climate System (1)


Complexity of the Climate System (2)Complexity of the Climate System (2)


MomentumEnergyWater

Land Surface

Coupler

Sun Atmosphere

Ocean

concentrations(GHG, SO4)

Source: MPI-M

Physical climate modelPhysical climate model


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


Model ResolutionModel Resolution


Grid ConfigurationsGrid Configurations

Example: curvilinear 302x132 grid configuration used with the HOPE-C ocean model.


Time ScalesTime Scales


Ensemble SimulationsEnsemble Simulations


Data Structures (1)Data Structures (1)

• What does this all mean in regard to the visualization of the data?


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)


Advanced VisualizationAdvanced Visualization

• What is “Low-End”-Visualization?


Low End Example: Animation with Low End Example: Animation with vcdatvcdatECHAM5 T159

480 x 240,

104 Time Steps


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


Examples (1) one 2Examples (1) one 2--D scalarD scalarECHAM5 /

MPI-OM

Software:

AVS/Express


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)


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)


Examples (4): two (four) 2Examples (4): two (four) 2--D scalars D scalars ECHAM5 / MPI-OM

T63 (~180km)

Software:

AVS/Express

Adobe After Effects


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


Examples (5): five 3Examples (5): five 3--D scalarsD scalarsECHAM5-HAM

T63 (~180km)

6-hourly

1 year

~ 10 Gbyte

Software:

AVS/Express


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)


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


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


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



• Availability of (new) methods and algorithms• Example: Shadows enhance the depth perception



Example: Flow Visualization

usingLEA,

Stream Surfaces,LEA + Stream Surf.

(Erlebacher, Jobard,

Weisskopf, 2004)


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?!


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

http://www.dkrz.de/

http://www.mpg.de/bilderBerichteDokumente/dokumentation/jahrbuch/2004/dkrz/forschungsSchwerpunkt/index.html

http://www.mpg.de/bilderBerichteDokumente/dokumentation/jahrbuch/2004/dkrz/forschungsSchwerpunkt/index.html

http://www.csar.cfs.ac.uk/about/csarfocus/focus10/vis_earth.pdf

http://enviroinfo.isep.at/UI 98/PDF - UI-98/710-721 B%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?


The urban climate, a stake for tomorrow?

- Urban climate : description- Urban climate : explanation- Research on urban climate- Impact of climate change- Conclusion


Urban climate : descriptionEvolution of climatic parameters

Temperature evolution between 1800 and 1970 in Paris (Montsouris) (Source : Dettwiller, 1978)



Number of frost days during the twentieth century in Paris (Montsouris)(Source : www.meteo.fr)



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)


Urban climate :descriptionDifference between urban and rural areas

(Source : www.gvrd.bc.ca)


Urban climate :descriptionDifference between urban and rural areas

Section NW-SE with the minimal temperatures in winter (1971-1980)

(Source : Colombert, 2005)


Urban climate : descriptionDifference between urban and rural areas

Number of frost days (Source : Escourrou, 1986)



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)




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



Increase of rainfall because of urbanization (record of the 6th of June 1978). (Source : Escourrou)

Wind



The wind :> Decrease of the speed> More important Turbulence> Creation of local winds

(Source : http://www.islandnet.com/~see/weather/elements/citywind.htm)


Urban climate : explanation

Consequences of urban activitiesLocal modification of surfaces characteristicsExternal context



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)


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)



Capture of radiation by town in comparison with the case of rural area (Sacré, 1983)



Modification of water balance

Towncenter

Sensible heat

80 %

Latent heat

Sensible heat

Latent heat

20%

Rural area withvegetation

14%

86%


(Source : Cantat, 1993)


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


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 ?


Climate change impact

(Source : IPCC)



Space conditioning demand [Danny Parker, FSEC (Florida Solar Energy Center) (http://www.iea.org/)]



Climate change andits interactions withother global problems

Urban climate is not yetclearly addressed

(Source : « le défi de la Terre »)


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



The urban climate, a stake for tomorrow?

Thank you for your attention

Morgane Colombert C.S.T.B.

Services, Process, Innovation Center [email protected]


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

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics

Anforderungen an Gebäude durch langfristige Klimaveränderung

S. Hoffmann, O. Kornadt CIB Weimar, 01.06.2006

DWD-TRYs and HadRM3-TRYs -

a comparison for Munich and Weimar

Sabine Hoffmann, Oliver Kornadt

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



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

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



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!

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



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)

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



WEIMAR: 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

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



WEIMAR: Maximum and minimum temperature

spring

summer

winter

autumn

-30

-20

-10

0

10

20

30

40

50

tem

pera

ture

[°C

]

DWD-TRY Had RM3

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



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

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



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

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



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

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



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

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



WEIMAR: Average temperature HadRM3

yearautumn wintersummerspring-5

0

5

10

15

20

25

tem

pera

ture

[°C

]

1970 2020 2050 2080

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



MUNICH: Average temperature HadRM3

yearautumn wintersummerspring-5

0

5

10

15

20

25

tem

pera

ture

[°C

]

1970 2020 2050 2080

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



WEIMAR: Maximum temperature HadRM3

autumn wintersummerspring5

10

15

20

25

30

35

40

45

50

tem

pera

ture

[°C

]

1970 2020 2050 2080

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



MUNICH: Maximum temperature HadRM3

spring summer winterautumn5

10

15

20

25

30

35

40

45

50

tem

pera

ture

[°C

]

1970 2020 2050 2080

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



0

10

20

30

40

50

60

30.7. 6.8. 13.8. 20.8. 27.8.

München 2080 Weimar 2080

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



WEIMAR: Minimum temperature HadRM3

spring summer winterautumn-15

-10

-5

0

5

10

15

tem

pera

ture

[°C

]

1970 2020 2050 2080

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



MUNICH: Minimum temperature HadRM3

spring summer winterautumn-15

-10

-5

0

5

10

15

tem

pera

ture

[°C

]

1970 2020 2050 2080

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



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

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



MUNICH: Global radiation HadRM3

yearautumn wintersummerspring0

50

100

150

200

250

300

glob

al ra

diat

ion

[W/m

²]

1970 2020 2050 2080

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



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

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



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

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



1970 2020 2050 2080

-20

-10

0

10

20

30

40

50

tem

pera

ture

[°C

]

Weimar Munich

Climate Change Weimar ↔ Munich

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



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

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



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

Bau

haus

-Uni

vers

ityW

eim

arD

epar

tmen

t of B

uild

ing

Phys

ics



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


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 link ing days

14R-1 linking days

Q -S in


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 link ing days

14R-1 linking days

Q -S in


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


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


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]


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.


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


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.


Predicted and ascertained Vote in HVAC-controlled Buildings

indo

orop

erat

ive

tem

pera

ture

[°C

]

mean monthly outdoor air temperature [°C]


Predicted and ascertained Vote in naturally ventilated Buildings

indo

orop

erat

ive

tem

pera

ture

[°C

]

mean monthly outdoor air temperature [°C]


Comfort Temperature in naturally ventilated Buildings according to ASHRAE 55


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.


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

20.0

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)


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


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


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


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


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 !!!


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


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):


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

MÜLLER-BBM

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 !


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.

CIB Membership offers:• international networking between academia, R&D organisations and industry• participation in local and international CIB conferences, symposia and seminars• CIB special publications and conference proceedings• R&D collaboration

Membership: CIB currently numbers over 400 members originating in some 70 countries, with very different back-grounds: major public or semi-public organisations, research institutes, universities and technical schools, documentation centres, firms, contractors, etc. CIB members include most of the major national laboratories and leading universities around the world in building and construction.

Working Commissions and Task Groups: CIB Members participate in over 50 Working Commissions and Task Groups, undertaking collaborative R&D activities organised around:• construction materials and technologies• indoor environment• design of buildings and of the built environment• organisation, management and economics• legal and procurement practices

Networking: The CIB provides a platform for academia, R&D organisations and industry to network together, as well as a network to decision makers, government institution and other building and construction institutions and organisations. The CIB network is respected for its thought-leadership, informa-tion and knowledge.

The CIB has formal and informal relationships with, amongst others: the United Nations Environmental Programme (UNEP); the European Commission; the European Network of Build-ing Research Institutes (ENBRI); the International Initiative for Sustainable Built Environment (iiSBE), the International Organization for Standardization (ISO); the International Labour Organization (ILO), International Energy Agency (IEA); International Associations of Civil Engineering, including ECCS, fib, IABSE, IASS and RILEM.

Conferences, Symposia and Seminars: CIB conferences and co-sponsored conferences cover a wide range of areas of interest to its Members, and attract more than 5000 partici-pants worldwide per year.

Leading conference series include:• International Symposium on Water Supply and Drainage for Buildings (W062)• Organisation and Management of Construction (W065)• Durability of Building Materials and Components (W080, RILEM & ISO)• Quality and Safety on Construction Sites (W099)• Construction in Developing Countries (W107)• Sustainable Buildings regional and global triennial conference series (CIB, iiSBE & UNEP)• Revaluing Construction• International Construction Client’s Forum

CIB Commissions (April 2007)TG33 Collaborative Engineering TG43 Megacities TG49 Architectural Engineering TG50 Tall Buildings TG53 Postgraduate Research Training in Building and ConstructionTG56 Macroeconomics for Construction TG57 Industrialisation in Construction TG58 Clients and Construction Innovation TG59 People in Construction TG61 Benchmarking Construction Performance DataTG62 Built Environment Complexity TG63 Disasters and the Built EnvironmentTG64 Leadership in ConstructionTG65 Small Firms in ConstructionTG66 Energy and the Built EnvironmentW014 Fire W018 Timber Structures W023 Wall Structures W040 Heat and Moisture Transfer in Buildings W051 Acoustics W055 Building Economics W056 Sandwich Panels W060 Performance Concept in Building W062 Water Supply and Drainage W065 Organisation and Management of Construction W069 Housing Sociology W070 Facilities Management and Maintenance W077 Indoor Climate W078 Information Technology for Construction W080 Prediction of Service Life of Building Materials and ComponentsW083 Roofing Materials and SystemsW084 Building Comfortable Environments for All W086 Building Pathology W089 Building Research and Education W092 Procurement Systems W096 Architectural Management W098 Intelligent & Responsive Buildings W099 Safety and Health on Construction Sites W101 Spatial Planning and infrastructure Development W102 Information and Knowledge Management in BuildingW104 Open Building Implementation W106 Geographical Information Systems W107 Construction in Developing Countries W108 Climate Change and the Built Environment W110 Informal Settlements and Affordable Housing W111 Usability of WorkplacesW112 Culture in ConstructionW113 Law and Dispute ResolutionW114 Earthquake Engineering and BuildingsW115 Construction Materials StewardshipW116 Smart and Sustainable Built Environments

PAGE 1

International Council for Research and Innovation in Building and Construction

Publications: The CIB produces a wide range of special publications, conference proceedings, etc., most of which are available to CIB Members via the CIB home pages. The CIB network also provides access to the publications of its more than 400 Members.

Recent CIB publications include:• Guide and Bibliography to Service Life and Durability Research for Buildings and Components (CIB 295)• Performance Based Methods for Service Life Prediction (CIB 294)• Performance Criteria of Buildings for Health and Comfort (CIB 292)• Performance Based Building 1st International State-of-the- Art Report (CIB 291)• Proceedings of the CIB-CTBUH Conference on Tall Buildings: Strategies for Performance in the Aftermath of the World Trade Centre (CIB 290)• Condition Assessment of Roofs (CIB 289)• Proceedings from the 3rd International Postgraduate Research Conference in the Built and Human Environment• Proceedings of the 5th International Conference on Performance-Based Codes and Fire Safety Design Methods• Proceedings of the 29th International Symposium on Water Supply and Drainage for Buildings• Agenda 21 for Sustainable Development in Developing Countries

R&D Collaboration: The CIB provides an active platform for international collaborative R&D between academia, R&D organisations and industry.

Publications arising from recent collaborative R&D ac-tivities include:• Agenda 21 for Sustainable Construction• Agenda 21 for Sustainable Construction in Developing Countries• The Construction Sector System Approach: An International Framework (CIB 293)• Red Man, Green Man: A Review of the Use of Performance Indicators for Urban Sustainability (CIB 286a)• Benchmarking of Labour-Intensive Construction Activities: Lean Construction and Fundamental Principles of Working Management (CIB 276)• Guide and Bibliography to Service Life and Durability Research for Buildings and Components (CIB 295)• Performance-Based Building Regulatory Systems (CIB 299)• Design for Deconstruction and Materials Reuse (CIB 272)• Value Through Design (CIB 280)

A recent major CIB collaborative activ-ity was the Thematic Network PeBBu Performance Based Building: a four-year programme that included 50 member organisations, that was coordinated by CIB and that was funded through the European Commission Fifth Framework Programme.

Themes: The main thrust of CIB activities takes place through a network of around 50 Working Commissions and Task Groups, organised around three CIB Priority Themes:• Sustainable Construction• Performance Based Building• Revaluing Construction

A fourth priority Theme, Integrated Design Solutions is cur-rently being developed within CIB.

CIB Annual Membership Fee 2005/07

Membership Fee (Euro)

Category 2005 2006 2007

FM1 10.019 10.270 10.526FM2 6.680 6.847 7.018FM3 2.297 2.354 2.413AM1 1.154 1.183 1.213AM2 703 773 851IM 229 235 241

The lowest Fee Category an organisation can be in depends on the organisation’s profile:FM1 Full Member Multi disciplinary building research institutes of national standing having a broad field of researchFM2 Full Member Medium size research Institutes; Public agencies with major research interest; Companies with major research interestFM3 Full Member Information centres of national standing; Organisations normally in Category AM1 or AM2 which prefer to be a Full MemberAM1 Associate Member Sectoral research & documentation institutes; Institutes for standardisation; Companies, consultants, contractors etc.; Professional associationsAM2 Associate Member Departments, faculties, schools or colleges of universities or technical Institutes of higher education (Universities only)IM Individual Member Individuals having an interest in the activities of CIB (not representing an organisation)

Fee Reduction: A reduction is offered to all fee levels in the magnitude of 50% for Members in countries with a GNIpc less than USD 1000 and a reduction to all fee levels in the magni-tude of 25% for Members in countries with a GNIpc between USD 1000 to 7000, as defined by the Worldbank.

For more information contact CIB General Secretariat:e-mail: [email protected]

PO Box 1837, 3000 BV Rotterdam, The NetherlandsPhone +31-10-4110240;Fax +31-10-4334372Http://www.cibworld.nl

PAGE 2

DISCLAIMER

All rights reserved. No part of this book may be reprinted or

reproduced or utilized in any form or by any electronic,

mechanical, or other means, now known or hereafter

invented, including photocopying and recording, or in any

information storage or retrieval system without

permission in writing from the publishers.

The publisher makes no representation, express or implied,

with regard to the accuracy of the information contained in this book

and cannot accept any legal responsibility or liability in whole or in part

for any errors or omissions that may be made.

The reader should verify the applicability of the information to

particular situations and check the references prior to any reliance

thereupon. Since the information contained in the book is multidisciplinary,

international and professional in nature, the reader is urged to consult with

an appropriate licensed professional prior to taking any action or making

any interpretation that is within the realm of a licensed professional practice.

w108 - climate change and the built environmentsite.cibworld.nl/dl/publications/w108.pdf ·...

Documents