Climate for Culture: assessing the impact of climate change on the future indoor climate in historic buildings using simulations
- Johanna Leissner†1Email authorView ORCID ID profile,
- Ralf Kilian†1,
- Lola Kotova†2,
- Daniela Jacob†2,
- Uwe Mikolajewicz†3,
- Tor Broström†4,
- Jonathan Ashley-Smith†5,
- Henk L. Schellen†6,
- Marco Martens†6,
- Jos van Schijndel†6,
- Florian Antretter†1,
- Matthias Winkler†1,
- Chiara Bertolin†7,
- Dario Camuffo†7,
- Goran Simeunovic†8 and
- Tomáš Vyhlídal†8
© Leissner et al. 2015
Received: 28 May 2015
Accepted: 24 November 2015
Published: 11 December 2015
The present study reports results from the large-scale integrated EU project “Climate for Culture”. The full name, or title, of the project is Climate for Culture: damage risk assessment, economic impact and mitigation strategies for sustainable preservation of cultural heritage in times of climate change. This paper focusses on implementing high resolution regional climate models together with new building simulation tools in order to predict future outdoor and indoor climate conditions. The potential impact of gradual climate change on historic buildings and on the vast collections they contain has been assessed. Two moderate IPCC emission scenarios A1B and RCP 4.5 were used to predict indoor climates in historic buildings from the recent past until the year 2100. Risks to the building and to the interiors with valuable artifacts were assessed using damage functions. A set of generic building types based on data from existing buildings were used to transfer outdoor climate conditions to indoor conditions using high resolution climate projections for Europe and the Mediterranean.
The high resolution climate change simulations have been performed with the regional climate model REMO over the whole of Europe including the Mediterranean region. Whole building simulation tools and a simplified building model were developed for historic buildings; they were forced with high resolution climate simulations. This has allowed maps of future climate-induced risks for historic buildings and their interiors to be produced. With this procedure future energy demands for building control can also be calculated.
With the newly developed method described here not only can outdoor risks for cultural heritage assets resulting from climate change be assessed, but also risks for indoor collections. This can be done for individual buildings as well as on a larger scale in the form of European risk maps. By using different standardized and exemplary artificial buildings in modelling climate change impact, a comparison between different regions in Europe has become possible for the first time. The methodology will serve heritage owners and managers as a decision tool, helping them to plan more effectively mitigation and adaption measures at various levels.
Climate change modelling and simulations
The most of the observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas (GHG) concentrations. The time-dependent (over centuries) climate response to changing concentration of GHGs can be studied using global circulation models (GCM). GCMs have been developed as a mathematical representation of the Earth system, which are not only coupled atmosphere–ocean general circulation models, but also take into account different biogeochemical feedbacks. Global climate models are forced with different future emission scenarios. These scenarios are developed by the Intergovernmental Panel on Climate Change (IPCC) and described for example how the future population will grow or which technologies will be applied to reduce CO2 emissions. Despite further development, current GCMs provide information only at a relative coarse spatial scale which is not sufficient for evaluating the impact of climate change on historic buildings. Thus high resolution regional climate models were needed. The regional climate model REMO  provided regional climate change projections for entire Europe at 12.5 km spatial resolution. Two moderate emission scenarios, the A1B scenario  and the very recent RCP4.5 scenario of the IPCC assessment report 5 (AR5)  were applied. For the mid-line A1B scenario, a CO2 emission increase is assumed until 2050 and a decrease afterwards. The second scenario—the RCP 4.5—stands for Representative Concentration Pathway (RCP) and is a scenario of long-term, global emissions of greenhouse gases, short-lived species, and land-use-land-cover which stabilizes radiative forcing at 4.5 Watts per meter squared (W/m2), approximately 650 ppm CO2 equivalent) in the year 2100 without ever exceeding that value.
Climate indices provided by climate modelling
Total precipitation (normal rain)
Global counter radiation
Whole building simulation models combine thermal building simulation with the hygrothermal component simulation. These models take into account the type of use (e.g. visitors, events) and HVAC (heating, ventilation and air conditioning) climatisation to assess the indoor environment. Different software tools have been systematically evaluated and the most useful ones for historic buildings were Hambase [9, 10] and WUFIplus [8, 11, 12]. The results of hygrothermal whole building simulation cover the whole range of hourly energy demand for building conditioning for each zone, hourly indoor temperature and relative humidity for comfort and damage assessment as well as hygrothermal conditions on and in the envelope components to assess hygric issues like mould growth . Thus, the full building simulations give a better representation of the hygrothermal performance of the building but this is at a high cost of developing the model and relatively long times for computing.
A second but simplified approach using state-space-models as transfer functions between the outdoor and indoor conditions was also applied for the prediction of indoor temperature and relative humidity [13, 14]. The simplified hygrothermal building model is a simple mathematical function that calculates the indoor climate from the outdoor climate. The function is derived from a statistical analysis of measurements. This method can only be applied when all necessary measured values are available for parameterization of the model. But the simulation performance of this method is easier to set up and the computing time is so short that simulations can even be made online. This has made it possible to perform simulations for different generic building types on a fine grid over Europe for different time periods to produce indoor climate and indoor climate risk maps. So far, the simplified model is limited to buildings without active climate control.
From outdoor climate simulation to future indoor climates and risk assessment
Outdoor climate predictions and outdoor risk maps
Climate change is mainly associated with the greenhouse gas carbon dioxide. The concentrations of CO2 in the atmosphere are increasing at an accelerating rate from decade to decade although many endeavours have been made to decrease the global emissions. The latest atmospheric CO2 data is consistent with a continuation of this long-standing trend and has reached 400.26 ppm in February 2015 . This is causing the planet to warm up and the earth`s average temperature has risen by 0.8 °C over the past century. Climate model projections summarized in Assessment Report AR5 of the IPCC  indicated that during the 21st century the global surface temperature is likely to rise a further 0.3–1.7 °C for their lowest emission scenario using stringent mitigation and 2.6–4.8 °C for their highest. Even small changes in the average temperature can translate to large and potentially dangerous shifts in climate and weather. The main focus of this project was given to the gradual changes of climate change and not to extreme events: This was excluded for this study by the European Commission`s 2008 call for projects.
The expected changes in yearly total precipitation (mm/year), in percentage terms, have been evaluated for the A1B emission scenario for the near and far future (Fig. 5, left and right). Both periods predict no or small changes from 0 to 20 % in Northern Europe (i.e. European Russia, Poland and Scandinavian Peninsula) and Central-Eastern Europe (i.e. Germany, Austria, Switzerland, Hungary, Czech Republic, Slovakia, and Ukraine). In the region of the European Atlantic Coast (i.e. Island, UK, Ireland and France) and Mediterranean regions the prediction highlights a mixed situation with both negative and positive changes up to +50 % in Egypt, Libya and Eastern Algeria and −50 % in central Portugal, Morocco and Western Algeria.
Salt crystallization cycles
Sea level rise
Frost days index
Dry days index
Wet days index
Heavy precipitation Index
Tropical day index
Future indoor climates and risk assessment
Figure 9 highlights the geographical distribution of indoor temperature changes over the whole of Europe: An increase in temperature range can be expected in Sweden and Norway, Denmark, Holland, central Romania, the Alps, Italy, on the coast of the former Yugoslavia and Greece. The rest of Europe will experience a decrease in temperature range. In the far future the scenario changes a bit, with the identification of two macro-areas: the first is Northern Europe, comprising also Germany and Poland with a decrease in indoor temperature range up to 5 °C and the second area constituting the Western and Southern Europe, with an increase up to 4 °C in indoor temperature range.
To assess the impact of climate change on the indoor climate of historical buildings statistical parameters of the three indoor climate variables temperature, relative humidity and mixing ratio were evaluated on annual and monthly time-scales. For all three variables the statistical parameters mean, maximum, minimum and range are calculated for both time-scales. Damages to the building interior and objects inside the buildings are often related to high levels or fluctuations in indoor temperature and relative humidity. Damage functions and risk thresholds can be used to describe the damage processes and give advice about possible dangers coming from indoor climate conditions. Thus, existing damage functions and risk thresholds were identified and associated to three types of damage: Biological, chemical and mechanical damage. For some damage functions available risk thresholds could be used for a risk categorisation into small, medium and high risks .
The above described evaluation methods for indoor climate variables and damage functions allow an assessment on an annual, seasonal and monthly time-scale. As the simulations of the generic sacred buildings cover a time-span of 31 years, a methodology for a long-term assessment had to be developed: For every simulated year all indoor climate variables, the damage functions and risk categories are assessed independently . Subsequently, the assessment results for every parameter are averaged, resulting in a characteristic value representing the time-span like for example the average annual salt-crystallization cycles or the average indoor temperature for the month August. To allow an averaging of the risk categories, these are translated into numeric values: Small risk is represented with “0”, medium risk with “1” and high risk with “2”. After averaging, the resulting value gives a long-term tendency towards a risk category.
The assessment has been performed for 474 locations which are distributed on a regular grid across Europe and the Mediterranean area, see Fig. 2. The assessments for the long-term damage risks and the indoor climate are applied to every location. Their results are the basis for the pan-European maps, which display the results of the risk assessment. Furthermore the changes between the time periods are also calculated and visualised as maps. The recent past serves as baseline for each of the two future periods.
Risk = (probability of finding a certain number of cycles) × (specific damage calculated with the damage function for each particular material)] which is the same as [Risk = (calculated frequency of cycles) × (damage for a single cycle calculated with the damage function for each particular material)]. Climate change will only affect the calculated frequency of crystallization. For each material, the ratio between the damage that will occur in a selected 30 year period (e.g. Near Future, Far Future) and the damage occurred in a certain reference period (e.g. 1961–1990) equals the ratio of the two calculated frequencies. It is thus possible to establish future tendencies, i.e. whether the damage is likely to increase, remain unchanged, or decrease, irrespective of the particular material type.
Figure 11 left shows for the near future under the A1B emission scenario, mixed situations in Northern Europe and the Mediterranean Area with changes ranging from (−) 20 cycles/year, in the Alps and in large part of Northern Europe, up to (+) 10 cycles/year respect to the recent past in the rest of Northern Europe, Southern Mediterranean and Spanish coast. In the far future (Fig. 11, right), the simulation shows larger changes, i.e. in the Northern region and on the Alps a decrease up to (−) 40 cycles/year of salt crystallization cycles and consequently less risk for mechanical damages on masonry and stones is predicted. In contrast, the salt-crystallization cycles increase in frequency at around (+) 10 cycles/year in the rest of Europe.
Changing climate conditions also will affect biological activity in historic buildings and on cultural heritage materials. Fungal growth is a widespread problem with implications for or human health and the integrity of heritage material. The effects on heritage items can vary from a light powdery dusting to severe staining, weakening and disintegration of substrate material. Many deterioration processes are accompanied by biochemical transformations that occur only at certain temperatures, in the growing phase as well as in the development phase of the organism.
The most important factors are thus temperature, humidity and the nature of the substrate. It is assumed that mould spores are ubiquitous. At temperatures above 0 °C and humidity levels above 70 % RH mould spores can germinate. The time to germination decreases as temperature and humidity rise. Most fungi grow in a temperature range from 0 to 50 °C, whereby the tolerance to low temperatures is better than to high temperatures in this range. Biological activity depends on temperature and a certain minimum humidity is required for growth at which not the total moisture, but the “free” available water is considered. This part is called water activity and is only the part of the water, which is not bound by soluble substances (such as salts, carbohydrates, or proteins). The water content of the substrate depends further on the chemical composition, the temperature and the pH-value of the substrate. Good growth conditions can be found not only whenever condensate is found at or on the material, but also at high relative humidity. Mould growth requires a certain minimum temperature to be active. To calculate the risk of mould growth the Sedlbauer isopleths system  has been used to generate the maps that show simulated risks in the recent past, near future and far future.
0: no growth
1: some growth detected only with microscopy
2: moderate growth detected with microscopy (coverage more than 10 %)
3: some growth detected visually
4: visually detected coverage more than 10 %
5: visually detected coverage more than 50 %
6: visually detected coverage 100 %
Mathematical modelling allows the mould index to be treated as a continuous variable rather than a series of discrete steps. The growth rate output of Sedlbauer’s biohygrothermal model has been correlated with the mould index . Risk levels have been arbitrarily set at points on this continuum.
Modelled indoor climates and future energy demand of the historic building Amerongen Castle
Historic buildings usually show elevated indoor humidity levels and a high variation of the climatic conditions, which can be dangerous to cultural heritage materials. This requires the detailed consideration of all hygrothermal interactions between the indoor air, the usage, the furnishing and the building envelope. The hygrothermal behaviour of a building component exposed to weather is an important aspect of the overall performance of a building. The calculation of the hygrothermal performance of a part of the envelope is state-of-the-art and a realistic assessment of all relevant effects can be carried out, but until now the total behaviour of the actual whole building is not accounted for. Questions which were addressed within this project:
How much ventilation and additional heat energy is required to ensure safe indoor conditions for cultural heritage when a historic building is exposed to extreme climate conditions or up to thousands of visitors per day? What will happen to the hygrothermal behaviour of walls and ceiling when a historic cellar is changed in its use and is turned for example into a restaurant? How do the indoor air conditions and the envelope of buildings with temporary use react to different heating and ventilation strategies? Can sorptive finish materials improve and stabilise the microclimate in historic buildings?
Case study: Amerongen Castle
Set points and capacity of conservation heating system
Minimum temperature set point for conservation heating
Maximum temperature set point for conservation heating
Minimum RH set point for conservation heating
Maximum RH set point for conservation heating
Maximum heating capacity
A new methodology has been developed to assess not only outdoor risks to cultural heritage assets, but also risks for indoor collections resulting from climate change. This can be done not only for individual buildings, but also on a larger scale in the form of risk maps: Altogether 55,650 thematic maps have been created. These address future outdoor and indoor climates until the year 2100, risks to cultural heritage objects such as mould growth or insect pests, and future energy demand for climate control in historic buildings. The maps can be produced using a generic building approach with an automated procedure. By using different artificial buildings as standard examples in modelling climate change impact, a comparison between different regions in Europe has become possible for the first time. The calculations have been done with two different methods, using transfer functions for a state space model or more elaborate whole building simulation models. Additionally, sustainable and energy-efficient mitigation and adaptation solutions based on the project methodology were tested and further developed. They used mathematical models of object responses to indoor-climate variations. Many of the results obtained are integrated into the decision support systems DMSS and ExDSS , offering useful information for heritage owners and the interested public. Although the final level of uncertainty in the risk maps will be high regardless of whether a deterministic or a probabilistic approach is used, risk maps based on state-of-the-art scientific knowledge are valuable as indicators of future risks to cultural heritage. They can play an important role as a decision tool helping to plan more effective mitigation and adaption measures at various levels.
All authors have contributed evenly to the results described in the publication. All authors read and approved the final manuscript.
The authors would like to thank the whole project team for their hard work and enthusiasm during the last five years. Only a few of the results have been presented here. Altogether around 90 scientists from 16 countries have worked on the project. We are also very grateful to the European Commission/DG Research and Innovation for funding the “Climate for Culture” project within the Seventh Framework Programme for Research under Grant Agreement No. 22 6973.
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Climate for Culture: http://www.climateforculture.eu. Accessed 21 Oct 2015.
- Kilian R, Leissner J, Antretter F, Holl K, Holm A. Modelling climate change impact on cultural heritage—The European project Climate for Culture. In: Bunnik T et al, editors Effect of climate change on built heritage. Pfaffenhofen: WTA Publications, Vol., 34, 2010. p. 131–143.Google Scholar
- Climate for Culture: Public Deliverables for WP1, WP3, WP4, WP5, WP7. http://www.climateforculture.eu/index.php?inhalt=furtherresources.projectresults. Accessed 19 Oct 2015.
- Jacob D, Elizalde A, Haensler A, Hagemann S, Kumar P, Podzun R, Rechid D, Remedio AR, Saeed F, Sieck K, Teichmann C, Wilhelm C. Assessing the transferability of the regional climate model REMO to different coordinated regional climate downscaling experiment (CORDEX) regions. Atmosphere. 2012;3:181–99.View ArticleGoogle Scholar
- IPCC SRES. In: Nakicenovic N, Swart R, editors. Special Report on Emissions Scenarios: a special report of Working Group III of the Intergovernmental Panel on Climate Change. UK: Cambridge University Press, 2000.Google Scholar
- Towards new scenarios for analysis of emissions, climate change, impacts and response strategies. Technical summary, IPCC expert meeting report 2007; https://www.ipcc.ch/pdf/supporting-material/expert-meeting-ts-scenarios.pdf. Accessed 2 Apr 2015.
- Bichlmair S, Kilian R, Krus M: Concept of a new airing strategy and simulation of the expected indoor climate in Linderhof Palace. In: CLIMA 2013: 11th REHVA World Congress 0& 8th International Conference on IAQVEC; Energy Efficient, Smart and Healthy Buildings; 2013, Prague: Czech Republic.Google Scholar
- Antretter F, Kilian R. WUFI® PLUS: Werkzeuge zur Vorhersage des hygrothermischen Raumklimas in historischen Gebäuden. Restauro. 2014;120:42–3.Google Scholar
- de Wit M. Hambase, Heat, air and moisture model for buildings and systems evaluation. Eindhoven University Press 2006. 2008; ISBN 90-6814-601-7 http://sts.bwk.tue.nl/hamlab/readers/bouwsteen100.pdf. Accessed 13 Apr 2015.
- van Schijndel AWM. A review of the application of SimuLink S-functions to multi domain modeling and building simulation. J Building Perform Simul. 2014;7(3):165–78.View ArticleGoogle Scholar
- Lengsfeld K, Holm A. Entwicklung und Validierung einer hygrothermischen Raumklima-Simulationssoftware WUFI® Plus. Bauphysik. 2007;29:178–86.View ArticleGoogle Scholar
- Kilian R: Klimastabilität historischer Gebäude. Bewertung hygrothermischer Simulationen im Kontext der Präventiven Konservierung. Dissertation, Universität Stuttgart, 2013.Google Scholar
- Kramer RP, van Schijndel AWM, Schellen HL. Inverse modeling of simplified hygrothermal building models to predict and characterize indoor climates. Build Environ. 2013;68:87–99.View ArticleGoogle Scholar
- Lankester P, Brimblecombe P. Future thermohygrometric climate within historic houses. J Cult Herit. 2012;12:1–6.View ArticleGoogle Scholar
- Earth’s CO2 Home Page. http://co2now.org/current-co2/co2-now/earths-home-page-for-atmospheric-co2.html. Accessed 2 Apr 2015.
- IPCC: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Geneva, Switzerland. http://ipcc.ch/report/ar5/syr/. Accessed 2 Apr 2015.
- Bertolin C, Camuffo D. Deliverable 5.2 Climate change impact on movable and immovable cultural heritage throughout Europe; 2014:15–18. http://www.climateforculture.eu/index.php?inhalt=furtherresources.projectresults. Accessed 21 Oct 2015.
- Winkler M. Development of an automated damage risk assessment method for the evaluation of the climate change impact on historic buildings and their interiors in Europe. Master’s Thesis. University of Applied Sciences Munich; 2013.Google Scholar
- Huijbregts Z, Kramer RP, Martens MHJ, van Schijndel AWM, Schellen HL. A proposed method to assess the damage risk of future climate change to museum objects in historic buildings. Build Environ. 2012;55:43–56.View ArticleGoogle Scholar
- Livingston, RA: Development of Air pollution damage functions. In: Baer NS, Snethlage R, editors. Saving our architectural heritage: The conservation of historic stone sculptures. New York: John Wiley 1996. p. 37–62.Google Scholar
- Brimblecombe P, Grossi CM. Millennium-long damage to building materials in London. Sci Total Environ. 2009;407:1354–61.View ArticleGoogle Scholar
- Sabbioni C, Brimblecombe P, Cassar M. The atlas of climate change impact on European cultural heritage: scientific analysis and management strategies. Anthem Press; 2012.Google Scholar
- Vereecken E, Roels S. Review of mould prediction models and their influence on mould risk evaluation. Build Environ. 2012;51:296–310.View ArticleGoogle Scholar
- Bratasz L: Acceptable and non-acceptable microclimate variability: the case of wood. In: Camuffo D, Fassina V and Havermans, editors. Basic environmental mechanisms affecting cultural heritage. Understanding deterioration mechanisms for conservation process, COST Action D 42. Chemical interactions between cultural artefacts and indoor environment. 2010. p. 49–58.Google Scholar
- Luxford N, Strlic M, Thickett D. Safe display parameters for veneer and marquetry objects: a review of the available information for wooden collections. Stud Conserv. 2013;58(No1):1–12.View ArticleGoogle Scholar
- Bratasz L. Allowable microclimatic variations for painted wood. Stud Conserv. 2013;58(2):65–79.View ArticleGoogle Scholar
- Brimblecombe P, Grossi CM. Potential damage to modern building materials from 21st century air pollution. Scient World J. 2010;10:116–25.View ArticleGoogle Scholar
- Bonazza A, Messina P, Sabbioni C, Grossi CM, Brimblecombe P. Mapping the impact of climate change on surface recession of carbonate buildings in Europe. Sci Total Environ. 2009;407:2039–50.View ArticleGoogle Scholar
- Menart E, De Bruin G, Strlic M. Dose-response functions for historic paper. Polym Degrad Stab. 2011;96:2029–39.View ArticleGoogle Scholar
- Ashley-Smith J : Deliverable 4.2 Report on damage functions in relation to climate change; 2013;21–38 http://www.climateforculture.eu/index.php?inhalt=furtherresources.projectresults. Accessed 21 Oct 2015.
- Martens MHJ. Climate risk assessment in museums: degradation risks determined from temperature and relative humidity data. Dissertation. Eindhoven University of Technology; 2012.Google Scholar
- Sedlbauer K. Prediction of mould fungus formation on the surface of and inside building components. Fraunhofer Institute for Building Physics, Holzkirchen, Germany; 2001. http://www.ibp.fraunhofer.de/content/dam/ibp/en/documents/ks_dissertation_etcm1021-30729.pdf; Accessed 2 Apr 2015.
- Viitanen H, Ojanen, T: Improved Model to Predict Mold Growth in Building Materials. ASHRAE, 2007.Google Scholar
- Ojanen T et al: Mold growth modeling of building structures using sensitivity classes of materials. ASHRAE, 2010.Google Scholar
- Krus M, Seidler CM, Sedlbauer K: Comparative evaluation of the predictions of two established mold growth models. ASHRAE Buildings XI Conference, 2010.Google Scholar
- Krus M, Seidler CM, Sedlbauer K: Übertragung des Mould-Indexes auf das Biohygrothermische Modell zur Schimmelpilzvorhersage. Gesundheitsingenieur 2011, 132(Heft 1): 32-36.Google Scholar
- Climate for Culture decision support system ExDSS. http://cfc.exdss.org/dss/riskcon. Accessed 15 Apr 2015.