Improved models, improved information? Exploring how climate change impacts pollen, influenza, and mold in Berlin and its surroundings

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Improved models, improved information? Exploring how climate change impacts pollen, influenza, and mold in Berlin and its surroundings. / Langendijk, Gaby S.; Rechid, Diana; Jacob, Daniela.

In: Urban Climate, Vol. 43, 101159, 01.05.2022.

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@article{4703205e95e24f14be942340d6a0bc99,
title = "Improved models, improved information? Exploring how climate change impacts pollen, influenza, and mold in Berlin and its surroundings",
abstract = "Urban decision makers rely on evidence-based climate information tailored to their needs to adequately adapt and prepare for future climate change impacts. Regional climate models, with grid sizes between 10–50 km, are a useful outset to understand potential future climate change impacts in urban regions. Recently developed convection-permitting climate models have grid sizes smaller than 5 km, and better resolve atmospheric processes related to the land surface like convection, and complex terrain such as cities. This study investigates how the convection-permitting model REMO simulates changes in climate conditions in the urban-rural context, compared to its conventional hydrostatic version. We analyze three impact cases: influenza spread and survival; ragweed pollen dispersion; and indoor mold growth. Simulations are analyzed for the near future (2041–2050) under emission scenario RCP8.5. Taking the Berlin region as a testbed, we show that the change signal (positive or negative impact) reverses for the 3 km compared to the 12.5 km grid resolution for the impact cases pollen, and mold, indicating added value. For influenza, the convection-permitting resolution intensifies the decrease of influenza days under climate change. The results show the potential of convection-permitting simulations to generate improved information about climate change impacts for urban regions to support decision making.",
keywords = "Added value, Berlin, Climate change impact, Convection-permitting model, Humidity, Influenza, Mold, Pollen, Regional climate model, Sustainability Governance, Environmental Governance",
author = "Langendijk, {Gaby S.} and Diana Rechid and Daniela Jacob",
year = "2022",
month = may,
day = "1",
doi = "10.1016/j.uclim.2022.101159",
language = "English",
volume = "43",
journal = "Urban Climate",
issn = "2212-0955",
publisher = "Elsevier B.V.",

}

RIS

TY - JOUR

T1 - Improved models, improved information? Exploring how climate change impacts pollen, influenza, and mold in Berlin and its surroundings

AU - Langendijk, Gaby S.

AU - Rechid, Diana

AU - Jacob, Daniela

PY - 2022/5/1

Y1 - 2022/5/1

N2 - Urban decision makers rely on evidence-based climate information tailored to their needs to adequately adapt and prepare for future climate change impacts. Regional climate models, with grid sizes between 10–50 km, are a useful outset to understand potential future climate change impacts in urban regions. Recently developed convection-permitting climate models have grid sizes smaller than 5 km, and better resolve atmospheric processes related to the land surface like convection, and complex terrain such as cities. This study investigates how the convection-permitting model REMO simulates changes in climate conditions in the urban-rural context, compared to its conventional hydrostatic version. We analyze three impact cases: influenza spread and survival; ragweed pollen dispersion; and indoor mold growth. Simulations are analyzed for the near future (2041–2050) under emission scenario RCP8.5. Taking the Berlin region as a testbed, we show that the change signal (positive or negative impact) reverses for the 3 km compared to the 12.5 km grid resolution for the impact cases pollen, and mold, indicating added value. For influenza, the convection-permitting resolution intensifies the decrease of influenza days under climate change. The results show the potential of convection-permitting simulations to generate improved information about climate change impacts for urban regions to support decision making.

AB - Urban decision makers rely on evidence-based climate information tailored to their needs to adequately adapt and prepare for future climate change impacts. Regional climate models, with grid sizes between 10–50 km, are a useful outset to understand potential future climate change impacts in urban regions. Recently developed convection-permitting climate models have grid sizes smaller than 5 km, and better resolve atmospheric processes related to the land surface like convection, and complex terrain such as cities. This study investigates how the convection-permitting model REMO simulates changes in climate conditions in the urban-rural context, compared to its conventional hydrostatic version. We analyze three impact cases: influenza spread and survival; ragweed pollen dispersion; and indoor mold growth. Simulations are analyzed for the near future (2041–2050) under emission scenario RCP8.5. Taking the Berlin region as a testbed, we show that the change signal (positive or negative impact) reverses for the 3 km compared to the 12.5 km grid resolution for the impact cases pollen, and mold, indicating added value. For influenza, the convection-permitting resolution intensifies the decrease of influenza days under climate change. The results show the potential of convection-permitting simulations to generate improved information about climate change impacts for urban regions to support decision making.

KW - Added value

KW - Berlin

KW - Climate change impact

KW - Convection-permitting model

KW - Humidity

KW - Influenza

KW - Mold

KW - Pollen

KW - Regional climate model

KW - Sustainability Governance

KW - Environmental Governance

UR - http://www.scopus.com/inward/record.url?scp=85127284187&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/1d2ce157-c3f2-3f3b-a70a-31f96059e595/

U2 - 10.1016/j.uclim.2022.101159

DO - 10.1016/j.uclim.2022.101159

M3 - Journal articles

AN - SCOPUS:85127284187

VL - 43

JO - Urban Climate

JF - Urban Climate

SN - 2212-0955

M1 - 101159

ER -

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