Framklim

Om Framklim

Name: Framklim – klimadata for bygningsfysiske analyser
Creator: SINTEF
License: https://creativecommons.org/licenses/by/4.0/

Testversjon. Tallmaterialet er ikke kvalitetssikret og må ikke brukes som grunnlag for prosjektering eller beslutninger.

Om tjenesten

Framklim utvikles for å gi historiske og framtidige klimadata for norske kommuner og værstasjoner til bruk i bygningsfysiske analyser fra tidlig planlegging til detaljprosjektering. Tjenesten kobler døgnverdier og framtidsscenarioer fra Klima i Norge 2025 med timesoppløste reanalyser, og leverer komplette timesoppløste klimafiler for bygningssimulering sammen med kartlag for utvalgte klimaindikatorer.

Framklim utvikles av SINTEF. Tjenesten er en testversjon som deles for tilbakemelding; se bruksvilkårene.

Slik lages dataene

Historisk referanse. Døgnverdier for temperatur og nedbør fra seNorge2018 (1 km) og referansedatasettet i Klima i Norge 2025 gir den historiske døgnreferansen for hvert sted. Referansedatasettet (seNorge2018 v20.05) forlenges fra 2020 til i dag med seNorge2018 v23.09, justert for konsistens med referansen.
Framtidsklima. Klima i Norge 2025 gir kvantiljusterte (EQM) døgnframskrivninger for scenarioet SSP3-7.0 fra ti modellkjeder, der globale klimamodeller fra CMIP6 er nedskalert med regionale klimamodeller.
Avhengighetskorreksjon. Framtidsseriene korrigeres deretter med metoden 3DBC for at samvariasjonen mellom variabler og over tid skal samsvare bedre med den historiske referanseserien i referanseperioden 1991–2020.
Timesoppløsning. Døgnverdiene bygges ut til timeserier med mønstre fra NORA3 (temperatur, nedbør, vind) og ERA5 (fuktighet, trykk, stråling). I tillegg kalibreres temperaturens døgnforløp mot MET Nordic Analysis.
Validering. De rekonstruerte historiske seriene kontrolleres mot kvalitetssikrede stasjonsobservasjoner fra Frost, Meteorologisk institutts tjeneste for observasjonsdata (frost.met.no).
Kartlag. Graddager, nullgjennomgangsdøgn, snø- og frostindikatorer og returverdier for varmeste 5-døgnsperiode og kaldeste 3-døgnsperiode beregnes på 1 km-rutenettet. For hver 250 m-rute velges deretter den 1 km-ruten som er mest representativ for rutens høyde. Rutenettet kartet vises på, er dermed finere enn den effektive oppløsningen i klimagrunnlaget.
Slagregnkart. Slagregnkartet følger en egen metodikk, utviklet i forskningsprosjektet CliVa, der vind nedskaleres til 50 m med hensyn til terrengets leside og eksponering. Kartet vises på 250 m-rutenettet, der hver rute er arealveid middelverdi av 50 m-rutene den dekker. Under verdien for et valgt punkt oppgis spredningen mellom disse 50 m-rutene, slik at det går fram hvor representativt middeltallet er for ruten. Verdien er slagregnindeksen etter ISO 15927-3 for en fritt eksponert vegg i åpent terreng. Standardkartet teller bare regn, det vil si timer der lufttemperaturen i NORA3 er minst 1 °C i 2 m høyde, fordi ISO 15927-3 er en indeks for slagregn. Et eget kart teller all nedbør, også snø. Som landsgjennomsnitt gir det omtrent dobbelt så høye verdier, og det er ikke standardens indeks. Terrengets virkning er allerede med i tallet; ruhet fra vegetasjon og bebyggelse, skjerming fra nabobygg og veggens egen geometri kommer i tillegg og kan korrigeres med egne reduksjonsfaktorer etter standarden.
Sommertemperatur. Siden viser temperaturen i den 50. varmeste timen hvert år 2014–2026, utetemperaturen veiledningen til TEK17 § 13-4 fortsatt viser til, og sammenligner den med høyeste 5-døgnsmiddel for valgt returperiode etter NS 3031:2025, som også finnes som kartlag for hvert punkt. 50-timerstemperaturen er beregnet fra timesverdier i MET Nordic Analysis v4 på 1 km og tilordnet 250 m-ruter med et høyde- og avstandsbevisst oppslag. MET Nordic Analysis korrigerer værmodellen MEPS med offisielle stasjoner og private værstasjoner (bl.a. Netatmo), de fleste i bebygde områder. MET Nordic Analysis gir verdier på 1 km-rutenett og er ikke det samme som en standardplassert stasjonsmåling; der analysen følger en offisiell stasjon, er verdiene stasjonens egne. Siden gir ikke døgnforløpet i dimensjonerende døgn, men viser til kommunesenterverdiene hos Klimadata for bygninger (NMBU).

Hvorfor SSP3-7.0?

Norsk klimatilpasningspolitikk bygger på føre-var-prinsippet. Norsk klimaservicesenter legger derfor det høye utslippsscenarioet SSP3-7.0 til grunn i de kommunevise klimaprofilene, og Framklim følger samme praksis med de samme ti modellkjedene og de samme referanseperiodene som Klima i Norge 2025. Meld. St. 33 (2012–2013) Klimatilpasning i Norge presiserer:

«Klimaframskrivninger er et viktig grunnlag for samfunnets tilpasning til klimaendringene. For å være føre var vil regjeringen at det i arbeidet med klimatilpasning legges til grunn høye alternativer fra de nasjonale klimaframskrivningene når konsekvensene av klimaendringene vurderes. Vektlegging av hensynet til klimaendringene skal i den enkelte sak balanseres opp mot andre viktige samfunnshensyn.»

Føre-var-prinsippet ble videreført i Meld. St. 26 (2022–2023).

Metoden beskrives steg for steg i bakgrunnshistoriene og i en metodeartikkel som er under arbeid.

Datakilder og kreditering

Framklim bygger på data fra tredjeparter. Rettighetene til underliggende data forblir hos rettighetshaverne, og lisensene og krediteringskravene under gjelder ved videre bruk.

Data som Framklim selv produserer, er lisensiert under CC BY 4.0. Krediter «Framklim, SINTEF» i tillegg til kildene under. Se bruksvilkårene.

Datasett	Bruk i Framklim	Produsent, lisens og kreditering
Klima i Norge 2025	Biasjusterte døgnframskrivninger (SSP3-7.0) og historisk referanse; framtidige kartlag.	Norsk klimaservicesenter (Meteorologisk institutt, NVE, NORCE og Bjerknessenteret). Bygger på globale klimamodeller fra CMIP6 og regionale nedskaleringer. Se Datagrunnlag for klimaframskrivninger.
seNorge2018	Døgnverdier for temperatur og nedbør (1 km). v20.05 via referansedatasettet i Klima i Norge 2025, v23.09 for forlengelsen fra 2020 til i dag. Grunnlag for graddager, nullgjennomgangsdøgn, snø- og frostindikatorer og returverdier for 5- og 3-døgnsmiddel.	Meteorologisk institutt. NLOD / CC BY 4.0. «Data fra Meteorologisk institutt.»
NORA3	Timesoppløste mønstre for temperatur, nedbør og vind; slagregnklimatologi.	Meteorologisk institutt. CC BY 4.0. «Basert på data fra Meteorologisk institutt.»
ERA5	Timesoppløste mønstre for fuktighet, trykk og stråling; døgnreferanse for fuktighet, trykk og stråling (kort- og langbølget) via HySN2018-ERA5 (NVE).	Copernicus Climate Change Service (C3S) / ECMWF. CC BY 4.0. «Contains modified Copernicus Climate Change Service information 2026. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.»
MET Nordic Analysis	Kalibrering av temperaturens døgnforløp; 50-timerstemperatur per år 2014–2026 (Sommertemperatur).	Meteorologisk institutt. CC BY 4.0.
Frost	Stasjonskatalog og kvalitetssikrede observasjoner til validering.	Meteorologisk institutt, frost.met.no. NLOD / CC BY 4.0.
Bakgrunnskart	Gråtonekart i kartvisningen og i bakgrunnshistoriene.	© Kartverket. CC BY 4.0.
Høydedata DTM 50 m	Terrengmodell for leside- og eksponeringsberegningen i slagregnlagene (50 m).	© Kartverket, Høydedata. CC BY 4.0.
Rutenett 250 m	Rutenettet kartlagene vises på, med terrenghøyde for valg av representativ 1 km-rute.	Statistisk sentralbyrå. NLOD.

Publikasjoner og referanser

Framklim-publikasjoner

Skeie, Gaarder & Time (2026). From Days to Hours: Evaluating Granularity of Weather Data for Building Physics Analysis. IOP Conference Series: Earth and Environmental Science 1615, 012062. doi:10.1088/1755-1315/1615/1/012062

Studier som bruker klimafilene

Asphaug, Skeie & Time (2026). Interior insulation of historic timber buildings: moisture risk under current and future climates. Nordic Symposium on Building Physics (NSB 2026). Akseptert for publisering.

Bakgrunn og forarbeid

Skeie, K. S. (2026). Tracking and Verifying Energy Performance of Norwegian Residential Buildings: Methods for modeling and information pooling by utilizing open datasets. Doktoravhandling, NTNU, 2026:228.
Skeie & Gustavsen (2025). Impact of kilometer-scale temperature and wind variability on simulated building heating demand. Preprint (ikke fagfellevurdert), SSRN. doi:10.2139/ssrn.5946028
Gaarder et al. (2023). Building Adaptation Measures Using Future Climate Scenarios—A Scoping Review of Uncertainty Treatment and Communication. Buildings 13(6), 1460. doi:10.3390/buildings13061460
Lisø, Kvande & Time (2017). Climate Adaptation Framework for Moisture-resilient Buildings in Norway. Energy Procedia 132, 628–633. doi:10.1016/j.egypro.2017.09.698

Datagrunnlag

Huang et al. (2026). An improved modelling chain for bias-adjusted high-resolution climate and hydrological projections for Norway. Geoscientific Model Development 19, 4567–4599. doi:10.5194/gmd-19-4567-2026
Eyring et al. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development 9, 1937–1958. doi:10.5194/gmd-9-1937-2016
Lussana et al. (2019). seNorge_2018, daily precipitation, and temperature datasets over Norway. Earth System Science Data 11, 1531–1551. doi:10.5194/essd-11-1531-2019
Haakenstad et al. (2021). NORA3: A nonhydrostatic high-resolution hindcast of the North Sea, the Norwegian Sea, and the Barents Sea. Journal of Applied Meteorology and Climatology 60, 1443–1464. doi:10.1175/JAMC-D-21-0029.1
Hersbach et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 146, 1999–2049. doi:10.1002/qj.3803
Erlandsen, Tallaksen & Kristiansen (2019). Merits of novel high-resolution estimates and existing long-term estimates of humidity and incident radiation in a complex domain. Earth System Science Data 11, 797–821. doi:10.5194/essd-11-797-2019

Metode

Framklims beregninger bygger på metodene under, men er tilpasset norske forhold og datagrunnlaget. De avviker derfor på enkelte punkter fra framgangsmåtene i standardene det vises til.

3DBC

Mehrotra & Sharma (2019). A resampling approach for correcting systematic spatiotemporal biases for multiple variables in a changing climate. Water Resources Research 55, 754–770. doi:10.1029/2018WR023270
Sharma, Mehrotra & Kusumastuti (2023). Correcting systematic bias in derived hydrologic simulations — implications for climate change assessments. Journal of Water and Climate Change 14(7), 2085–2102. doi:10.2166/wcc.2023.230

Typiske og ekstreme år

Pernigotto, Prada & Gasparella (2020). Extreme reference years for building energy performance simulation. Journal of Building Performance Simulation 13(2), 152–166. doi:10.1080/19401493.2019.1585477
Thiis, Petersen, Fuglestvedt & Hygen (2025). Typical meteorological years tailored for high latitudes based on high-resolution reanalysis data. Journal of Building Performance Simulation. doi:10.1080/19401493.2025.2573724 Metoden bak TMY-NO fra Meteorologisk institutt og NMBU, som ligger til grunn for de typiske månedene i NS 3031:2025: Klimadata for bygninger.

Fuktreferanseår

Geving, S. (1997). Moisture design of building constructions: Hygrothermal analysis using simulation models. Doktoravhandling, NTNU.
NS-EN 15026:2023. Hygrothermal performance of building components and building elements — Assessment of moisture transfer by numerical simulation, Annex B.
Vandemeulebroucke, I., Caluwaerts, S. & Van Den Bossche, N. (2022). Decision framework to select moisture reference years for hygrothermal simulations. Building and Environment, 218. doi:10.1016/j.buildenv.2022.109080

Returverdier for temperatur

Hygen, Grinde & Tajet (2023). Calculation of design temperatures: Proposed use of Bayesian GEV to calculate design summer and design winter temperatures. MET report 02/2023. Meteorologisk institutt.

Slagregn

Rydock, Lisø, Førland, Nore & Thue (2005). A driving rain exposure index for Norway. Building and Environment 40, 1450–1458. doi:10.1016/j.buildenv.2004.11.018
ISO 15927-3:2009. Hygrothermal performance of buildings — Calculation and presentation of climatic data — Part 3: Calculation of a driving rain index for vertical surfaces from hourly wind and rain data.
Byggforskserien (2023). Klimadata for dimensjonering mot regnpåkjenning. BKS 451.031, SINTEF. ISSN 2387-6328.

Frostsprenging og nullpunktskryssinger

Lisø, Kvande, Hygen, Thue & Harstveit (2007). A frost decay exposure index for porous, mineral building materials. Building and Environment 42, 3547–3555. doi:10.1016/j.buildenv.2006.10.022
Gaarder, Tajet, Dobler, Hygen & Kvande (2024). Future Climate Projections and Uncertainty Evaluations for Frost Decay Exposure Index in Norway. Buildings 14(9), 2873. doi:10.3390/buildings14092873

Råtefare

Lisø, Hygen, Kvande & Thue (2006). Decay potential in wood structures using climate data. Building Research & Information 34(6), 546–551. doi:10.1080/09613210600736248
Hygen, Øyen & Almås (2011). Assessment of climate vulnerability in the Norwegian built environment. Advances in Science and Research 6, 151–153. doi:10.5194/asr-6-151-2011
Tajet & Hygen (2017). Potential risk of wood decay. MET report 08/2017. Meteorologisk institutt.

Bakgrunnshistorier Eksperimentell demo

Fire interaktive historier viser metoden steg for steg, fra historiske referanser og klimaframskrivninger gjennom avhengighetskorreksjon til komplette timesoppløste værfiler. De er en eksperimentell demonstrasjon: forklaringene er forenklet, tallene er eksempler fra et lite arbeidssett, og sidene kan endres eller fjernes.

Finansiering

Framklim er utviklet av SINTEF Community med grunnbevilgningsmidler for 2024–2026. Metodikken for framtidsklima, med 3DBC-basert konstruksjon av timesoppløste klimafiler, er utviklet i dette arbeidet.

Datainfrastrukturen, temperaturkorreksjonen av timesoppløste reanalyser og valget av representativ rute bygger på doktorgradsarbeidet Tracking and Verifying Energy Performance of Norwegian Residential Buildings ved NTNU, Institutt for arkitektur og teknologi, finansiert gjennom Forskningssenter for nullutslippsområder i smarte byer (FME ZEN), Norges forskningsråd, med NTNU og SINTEF som forskningspartnere.

Slagregnkartet er utviklet i CliVa (Detect and mitigate climate change risk damage for optimal property value preservation), et innovasjonsprosjekt i næringslivet finansiert av Norges forskningsråd, prosjekt 355516.

Vilkår og kontakt

Bruken av tjenesten reguleres av bruksvilkårene. Spørsmål og tilbakemeldinger sendes til framklim@sintef.no.

Framklim

About Framklim

Test version. The figures have not been quality assured and must not be used as a basis for design or decisions.

The service

Framklim is being developed to provide historical and future climate data for Norwegian municipalities and weather stations for use in building physics analysis from early planning through detailed design. It connects daily data and future scenario projections from Klima i Norge 2025 with hourly reanalyses, and delivers complete hourly climate files for building simulation together with map layers for selected climate indicators.

Framklim is developed by SINTEF. The service is a test version shared for feedback; see the terms of use.

How the data is made

Historical reference. Daily temperature and precipitation from seNorge2018 (1 km) and the reference dataset of Klima i Norge 2025 give the historical daily reference for each location. The reference dataset (seNorge2018 v20.05) is extended from 2020 to the present with seNorge2018 v23.09, adjusted for consistency with the reference.
Future climate. Klima i Norge 2025 provides quantile-adjusted (EQM) daily projections for scenario SSP3-7.0 from ten model chains, in which global climate models from CMIP6 are downscaled with regional climate models.
Dependence correction. The future series are then corrected with the 3DBC method so that the co-variation between variables and over time agrees more closely with the historical reference series in the 1991–2020 reference period.
Hourly resolution. Daily values are expanded to hourly series with patterns from NORA3 (temperature, precipitation, wind) and ERA5 (humidity, pressure, radiation). In addition, the within-day temperature profile is calibrated against the MET Nordic Analysis.
Validation. The reconstructed historical series are checked against quality-controlled station observations from Frost, the Norwegian Meteorological Institute’s observation data service (frost.met.no).
Map layers. Degree days, zero-crossing days, snow and frost indicators and return levels of the hottest 5-day and coldest 3-day periods are computed on the 1 km grid. For each 250 m cell, the 1 km cell most representative of the cell’s elevation is then selected. The grid the map is drawn on is therefore finer than the effective resolution of the climate data.
Wind-driven rain map. The wind-driven rain map follows a separate method, developed in the CliVa research project, in which wind is downscaled to 50 m accounting for terrain lee and exposure. The map is shown on the 250 m grid, each cell being the area-weighted mean of the 50 m cells it covers. Beneath the value for a chosen point, the spread among those 50 m cells is given, so it is clear how representative the mean is of the site. The value is the ISO 15927-3 index for a freely exposed wall in open country. The default map counts rain only, that is hours in which the NORA3 air temperature at 2 m is at least 1 °C, since ISO 15927-3 is a driving-rain index. A second map counts all precipitation, snow included. On a national average its values are about twice as high, and it is not the standard’s index. The effect of the terrain is already in the figure; roughness from vegetation and buildings, shelter from neighbouring structures and the wall’s own geometry come on top and are corrected for according to the standard.
Summer temperature. The page shows the temperature of the 50th hottest hour of each year 2016–2025, the outdoor temperature the guidance to TEK17 § 13-4 still refers to, and compares it with the highest 5-day mean at a chosen return period under NS 3031:2025, which is also a map layer for every point. The 50-hour temperature is computed from hourly MET Nordic Analysis v4 values at 1 km and assigned to 250 m cells by an elevation- and distance-aware lookup. MET Nordic Analysis corrects the MEPS weather model with official stations and citizen weather stations (Netatmo among others), most of them in built-up areas. MET Nordic Analysis gives values on a 1 km grid and is not the same as a standard-sited station measurement; where the analysis follows an official station, the values are the station’s own. The page does not give the diurnal course of the design day; it refers to the municipal-centre values at Klimadata for bygninger (NMBU).

Why SSP3-7.0?

Norwegian climate adaptation policy rests on the precautionary principle. The Norwegian Centre for Climate Services therefore bases its municipal climate profiles on the high emission scenario SSP3-7.0, and Framklim follows the same practice with the same ten model chains and the same reference periods as Klima i Norge 2025. The government white paper Meld. St. 33 (2012–2013) Klimatilpasning i Norge states (our translation):

“Climate projections are an important basis for society’s adaptation to climate change. As a precaution, the Government wants climate adaptation work to be based on the high alternatives from the national climate projections when the consequences of climate change are assessed. In each individual case, the weight given to climate change must be balanced against other important societal considerations.”

The precautionary principle was carried forward in Meld. St. 26 (2022–2023).

The method is described step by step in the background stories and in a methods paper in preparation.

Data sources and credits

Framklim is built on third-party data. Rights to the underlying data remain with their rights holders, and the licences and attribution requirements below apply to any further use.

Data produced by Framklim itself are licensed under CC BY 4.0. Credit “Framklim, SINTEF” in addition to the sources below. See the terms of use.

Dataset	Use in Framklim	Producer, licence and credit
Klima i Norge 2025	Bias-adjusted daily projections (SSP3-7.0) and historical reference; future map layers.	Norwegian Centre for Climate Services (MET Norway, NVE, NORCE and the Bjerknes Centre). Built on CMIP6 global climate models and regional downscaling. See Datagrunnlag for klimaframskrivninger.
seNorge2018	Daily temperature and precipitation (1 km). v20.05 via the Klima i Norge 2025 reference dataset, v23.09 for the extension from 2020 to the present. Basis for degree days, zero-crossing days, snow and frost indicators and the 5-day and 3-day return levels.	MET Norway. NLOD / CC BY 4.0. “Data from the Norwegian Meteorological Institute.”
NORA3	Hourly patterns for temperature, precipitation and wind; wind-driven rain climatology.	MET Norway. CC BY 4.0. “Based on data from MET Norway.”
ERA5	Hourly patterns for humidity, pressure and radiation; daily reference for humidity, pressure and radiation (shortwave and longwave) via HySN2018-ERA5 (NVE).	Copernicus Climate Change Service (C3S) / ECMWF. CC BY 4.0. “Contains modified Copernicus Climate Change Service information 2026. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.”
MET Nordic Analysis	Calibration of the within-day temperature profile; 50-hour temperature per year 2016–2025 (Summer temperature).	MET Norway. CC BY 4.0.
Frost	Station catalogue and quality-controlled observations for validation.	MET Norway, frost.met.no. NLOD / CC BY 4.0.
Base map	Greyscale map in the location picker and the background stories.	© Kartverket. CC BY 4.0.
Høydedata DTM 50 m	Digital terrain model for the lee and exposure computation in the wind-driven rain layers (50 m).	© Kartverket, Høydedata. CC BY 4.0.
250 m grid	The grid the map layers are shown on, with terrain elevation used to select the representative 1 km cell.	Statistics Norway. NLOD.

Publications and references

Framklim publications

Skeie, Gaarder & Time (2026). From Days to Hours: Evaluating Granularity of Weather Data for Building Physics Analysis. IOP Conference Series: Earth and Environmental Science 1615, 012062. doi:10.1088/1755-1315/1615/1/012062

Studies using the climate files

Asphaug, Skeie & Time (2026). Interior insulation of historic timber buildings: moisture risk under current and future climates. Nordic Symposium on Building Physics (NSB 2026). Accepted for publication.

Background and precursor work

Skeie, K. S. (2026). Tracking and Verifying Energy Performance of Norwegian Residential Buildings: Methods for modeling and information pooling by utilizing open datasets. Doctoral theses at NTNU, 2026:228.
Skeie & Gustavsen (2025). Impact of kilometer-scale temperature and wind variability on simulated building heating demand. Preprint (not peer reviewed), SSRN. doi:10.2139/ssrn.5946028
Gaarder et al. (2023). Building Adaptation Measures Using Future Climate Scenarios—A Scoping Review of Uncertainty Treatment and Communication. Buildings 13(6), 1460. doi:10.3390/buildings13061460
Lisø, Kvande & Time (2017). Climate Adaptation Framework for Moisture-resilient Buildings in Norway. Energy Procedia 132, 628–633. doi:10.1016/j.egypro.2017.09.698

Data

Huang et al. (2026). An improved modelling chain for bias-adjusted high-resolution climate and hydrological projections for Norway. Geoscientific Model Development 19, 4567–4599. doi:10.5194/gmd-19-4567-2026
Eyring et al. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development 9, 1937–1958. doi:10.5194/gmd-9-1937-2016
Lussana et al. (2019). seNorge_2018, daily precipitation, and temperature datasets over Norway. Earth System Science Data 11, 1531–1551. doi:10.5194/essd-11-1531-2019
Haakenstad et al. (2021). NORA3: A nonhydrostatic high-resolution hindcast of the North Sea, the Norwegian Sea, and the Barents Sea. Journal of Applied Meteorology and Climatology 60, 1443–1464. doi:10.1175/JAMC-D-21-0029.1
Hersbach et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 146, 1999–2049. doi:10.1002/qj.3803
Erlandsen, Tallaksen & Kristiansen (2019). Merits of novel high-resolution estimates and existing long-term estimates of humidity and incident radiation in a complex domain. Earth System Science Data 11, 797–821. doi:10.5194/essd-11-797-2019

Methods

The Framklim calculations follow the methods below, but are adapted to Norwegian conditions and to the underlying data. They therefore deviate on some points from the procedures in the standards referred to.

3DBC

Mehrotra & Sharma (2019). A resampling approach for correcting systematic spatiotemporal biases for multiple variables in a changing climate. Water Resources Research 55, 754–770. doi:10.1029/2018WR023270
Sharma, Mehrotra & Kusumastuti (2023). Correcting systematic bias in derived hydrologic simulations — implications for climate change assessments. Journal of Water and Climate Change 14(7), 2085–2102. doi:10.2166/wcc.2023.230

Typical and extreme years

Pernigotto, Prada & Gasparella (2020). Extreme reference years for building energy performance simulation. Journal of Building Performance Simulation 13(2), 152–166. doi:10.1080/19401493.2019.1585477
Thiis, Petersen, Fuglestvedt & Hygen (2025). Typical meteorological years tailored for high latitudes based on high-resolution reanalysis data. Journal of Building Performance Simulation. doi:10.1080/19401493.2025.2573724 The method behind TMY-NO from the Norwegian Meteorological Institute and NMBU, which underpins the typical months in NS 3031:2025: Klimadata for bygninger.

Moisture reference years

Geving, S. (1997). Moisture design of building constructions: Hygrothermal analysis using simulation models. Doctoral thesis, NTNU.
NS-EN 15026:2023. Hygrothermal performance of building components and building elements — Assessment of moisture transfer by numerical simulation, Annex B.
Vandemeulebroucke, I., Caluwaerts, S. & Van Den Bossche, N. (2022). Decision framework to select moisture reference years for hygrothermal simulations. Building and Environment, 218. doi:10.1016/j.buildenv.2022.109080

Temperature return levels

Hygen, Grinde & Tajet (2023). Calculation of design temperatures: Proposed use of Bayesian GEV to calculate design summer and design winter temperatures. MET report 02/2023. Norwegian Meteorological Institute.

Wind-driven rain

Rydock, Lisø, Førland, Nore & Thue (2005). A driving rain exposure index for Norway. Building and Environment 40, 1450–1458. doi:10.1016/j.buildenv.2004.11.018
ISO 15927-3:2009. Hygrothermal performance of buildings — Calculation and presentation of climatic data — Part 3: Calculation of a driving rain index for vertical surfaces from hourly wind and rain data.
Byggforskserien (2023). Klimadata for dimensjonering mot regnpåkjenning. BKS 451.031, SINTEF. ISSN 2387-6328.

Frost decay and freeze–thaw cycles

Lisø, Kvande, Hygen, Thue & Harstveit (2007). A frost decay exposure index for porous, mineral building materials. Building and Environment 42, 3547–3555. doi:10.1016/j.buildenv.2006.10.022
Gaarder, Tajet, Dobler, Hygen & Kvande (2024). Future Climate Projections and Uncertainty Evaluations for Frost Decay Exposure Index in Norway. Buildings 14(9), 2873. doi:10.3390/buildings14092873

Wood decay risk

Lisø, Hygen, Kvande & Thue (2006). Decay potential in wood structures using climate data. Building Research & Information 34(6), 546–551. doi:10.1080/09613210600736248
Hygen, Øyen & Almås (2011). Assessment of climate vulnerability in the Norwegian built environment. Advances in Science and Research 6, 151–153. doi:10.5194/asr-6-151-2011
Tajet & Hygen (2017). Potential risk of wood decay. MET report 08/2017. Norwegian Meteorological Institute.

Background stories Experimental demo

Four interactive stories show the method step by step, from the historical reference and climate projections through dependence correction to complete hourly weather files. They are an experimental demonstration: the explanations are simplified, the figures are examples from a small working set, and the pages may change or be removed.

Funding

Framklim is developed by SINTEF Community with basic institutional funding for 2024–2026. The future-climate methodology, with 3DBC-based construction of hourly climate files, was developed in this work.

The data infrastructure, the temperature correction of hourly reanalyses and the representative grid-cell selection build on the doctoral thesis Tracking and Verifying Energy Performance of Norwegian Residential Buildings at NTNU, Department of Architecture and Technology, funded through the Research Centre on Zero Emission Neighbourhoods in Smart Cities (FME ZEN), the Research Council of Norway, with NTNU and SINTEF as research partners.

The wind-driven rain map was developed in CliVa (Detect and mitigate climate change risk damage for optimal property value preservation), an Innovation Project for the Industrial Sector funded by the Research Council of Norway, project 355516.

Terms and contact

Use of the service is governed by the terms of use. Questions and feedback may be sent to framklim@sintef.no.