A surface modelling approach for attribution and disentanglement of the effects of global warming from urbanization in temperature extremes: application to Lisbon
type: Nanopublication
has assertion: assertion
has provenance: provenance
has publication info: pubinfo
type: FAIRDigitalObject
type: RoCrateNanopub
created: 2026-05-12T13:04:19.791+02:00
creator: RoCrateBot

sig
has algorithm: RSA
signed by: RoCrateBot

Environmental research
type: DefinedTerm

A surface modelling approach for attribution and disentanglement of the effects of global warming from urbanization in temperature extremes: application to Lisbon
type: ResearchObject
type: LiveRO
type: Dataset
type: BibliographyResearchObject
about: Environmental research
content size: 0
date created: 2026-03-23 17:27:30.746575+00:00
date modified: 2026-03-25 14:43:04.015341+00:00
date published: 2026-03-23 17:27:30.746575+00:00
description: Abstract Attribution and disentanglement of the effects of global greenhouse gas and land-use changes on temperature extremes in urban areas is a complex and critical issue in the context of regional-to-local climate change mitigation and adaptation. Here, an innovative modelling framework based on a large ensemble of urban climate simulations, using SURFEX (a land-surface model) coupled to TEB (an urban canopy model), forced by E20C (a GCM-based reanalysis), is proposed, and applied to the capital of Portugal—Lisbon. This approach allowed to disentangle the main drivers of change of extreme temperatures in Lisbon, while also improving the simulated summer temperature variability compared to E20C, using station observations as reference. The improvements were physically linked to the strong sensitivity of summer mean and extreme temperatures to local land-use properties. The sensitivity was systematically investigated and robustly demonstrated here, with built-fraction (buildings + roads), albedo and emissivity emerging as key surface parameters. The results revealed a very strong summer temperature increase between 1951–1980 and 1981–2010 periods: 0.90 °C for daily maximum temperature (T max), and 0.76 °C for daily minimum temperature (T max). These changes were sensitive to considering different (but constant throughout the simulation) land-uses, varying by about 10% for T max, and around 17% for T min. Regarding the temperature extremes (quantified by extreme hot days, EHD, and extreme hot nights, EHN, respectively defined as exceeding the 95th-percentile of T max and T min) the changes and their dependencies with the land-use are much more drastic. The isolated effect of changing land-use (keeping the climate forcing unchanged) from rural/natural (low built-fraction) towards dense urbanization (high built-fraction) caused a significant increase in EHN (up to ∼+130 d per 30 years, larger than the effect due to climate forcing alone), and in EHD (∼+60 d per 30 yea…
encoding format: application/ld+json
license: no-permission
creation mode: MANUAL
cite-as: Gonzalez, Esteban. "A surface modelling approach for attribution and disentanglement of the effects of global warming from urbanization in temperature extremes: application to Lisbon." ROHub. Mar 23 ,2026. https://w3id.org/ro-id/ddf399fc-b532-4e4e-9b13-1796a7a144d7.

Esteban Gonzalez
type: Agent

sensitivity
type: Lemma
norm score: 13.960113960113961
score: 4.9

land-use property
type: Phrase
norm score: 27.24014336917563
score: 7.6

land-use
type: Lemma
norm score: 23.931623931623932
score: 8.4

E20C
type: Lemma
norm score: 13.675213675213675
score: 4.8

IPCC
type: FCID

User Needs (RAST)
type: FCID

Climate-ADAPT Adaptation Sectors
type: FCID

Geographical Scope
type: FCID

Engineering (General)
type: NASA

Geosciences (General)
type: NASA

E20C
type: Organization

climate
type: Concept
norm score: 5.759803921568627
score: 4.7

summer mean temperature
type: Phrase
norm score: 21.14695340501792
score: 5.9

https://w3id.org/ro-id/enrichment/2856738b-2c8c-4f47-aa90-4a2e3a61bd03
type: Sentence
name: This approach allowed to disentangle the main drivers of change of extreme temperatures in Lisbon, while also improving the simulated summer temperature variability compared to E20C, using station observations as reference.
norm score: 27.01525054466231
score: 12.4

per 30 years
type: TimeReference

Key Type Measures
type: FCID

disentanglement of the effect
type: Phrase
norm score: 21.505376344086024
score: 6.0

result
type: Concept
norm score: 6.61764705882353
score: 5.4

extrication
type: Concept
norm score: 6.25
score: 5.1

Lisbon abstract
type: Phrase
norm score: 13.261648745519715
score: 3.7

The improvements were physically linked to the strong sensitivity of summer mean and extreme temperatures to local land-use properties.
type: Sentence
norm score: 40.087145969498906
score: 18.4

Weather
type: IPTC
path: Weather

land-use
type: Concept
norm score: 14.338235294117649
score: 11.7

Mathematical Physics
type: FieldOfResearch

Funding
type: FCID

mean temperature
type: Lemma
norm score: 18.233618233618234
score: 6.4

Housing and urban planning policy
type: IPTC
path: Politics/Government policy/Interior policy/Housing and urban planning policy

Policy Scale
type: FCID

Lisbon
type: Concept
norm score: 10.784313725490199
score: 8.8

Environmental Science and Management
type: FieldOfResearch

T max
type: Phrase
norm score: 16.845878136200717
score: 4.7

A surface modelling approach for attribution and disentanglement of the effects of global warming from urbanization in temperature extremes: application to Lisbon Abstract
type: Sentence
norm score: 32.89760348583878
score: 15.1

emissivity
type: Concept
norm score: 5.637254901960784
score: 4.6

fraction
type: Concept
norm score: 6.004901960784315
score: 4.9

physics
type: Domain
norm score: 30.573248407643312
score: 4.8

Geosciences
type: NASA

Lisbon
type: Lemma
norm score: 18.233618233618234
score: 6.4

Meteorology and climatology
type: NASA

1981-2010 periods
type: TimeReference

temperature
type: Concept
norm score: 13.357843137254903
score: 10.9

Engineering
type: NASA

Mathematical Sciences
type: FieldOfResearch

mean temperature
type: Concept
norm score: 11.151960784313726
score: 9.1

Statistics
type: FieldOfResearch

Chemistry
type: IPTC
path: Science and technology/Natural science/Chemistry

Climate change
type: IPTC
path: Environment/Climate change

summer
type: TimeReference

Other Physical Sciences
type: FieldOfResearch

Environmental Sciences
type: FieldOfResearch

Methodology
type: FCID

Lisbon
type: Place
geonames: https://www.geonames.org/2267057
wikidata: Q597

Atmospheric Sciences
type: FieldOfResearch

between 1951-1980
type: TimeReference

Stakeholders
type: FCID

maximum
type: Concept
norm score: 5.514705882352941
score: 4.5

of summer
type: TimeReference

sensitivity
type: Concept
norm score: 8.333333333333334
score: 6.8

dependent territory
type: Concept
norm score: 6.25
score: 5.1

Climate Hazard
type: FCID

meteorology
type: Domain
norm score: 69.4267515923567
score: 10.9

Knowledge Sector (EEA)
type: FCID

Physical Sciences
type: FieldOfResearch

Portugal
type: Place
geonames: https://www.geonames.org/2264397
wikidata: Q45

Earth Sciences
type: FieldOfResearch

temperature extreme
type: Lemma
norm score: 11.965811965811966
score: 4.2

Fluid mechanics and thermodynamics
type: NASA

Climatology
type: FCID

Structural/physical: Ecosystem-based
type: FCID

Preparing the ground
type: FCID

City in Portugal
type: FCID

Academia/ Research Institutions
type: FCID

Physical and Technological
type: FCID

Extreme heat
type: FCID

Academic/ Institutional
type: FCID

No policy or regulation
type: FCID

none
type: FCID

Climate change impacts, risks and adaptation
type: FCID