Evidence map›Paper›PMID 40750609›Full record

ArticleScientific data2025

GeoMIP-Pattern - a pattern scaling dataset for efficient generation of custom geoengineering scenarios.

Rodrigo Muñoz-Sánchez, Bernardo A Bastien-Olvera, Oscar Calderón, Francisco Estrada Porrúa, Miguel Altamirano

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Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Rodrigo Muñoz-SánchezInstituto de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México, Av. Universidad 3004, Copilco Universidad, Coyoacán, 04510, Ciudad de México, CDMX, Mexico. rodrigo.munos@atmosfera.unam.mx.ORCID http://orcid.org/0000-0002-9200-0838
Bernardo A Bastien-OlveraInstituto de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México, Av. Universidad 3004, Copilco Universidad, Coyoacán, 04510, Ciudad de México, CDMX, Mexico.
Oscar CalderónInstituto de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México, Av. Universidad 3004, Copilco Universidad, Coyoacán, 04510, Ciudad de México, CDMX, Mexico.
Francisco Estrada PorrúaInstituto de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México, Av. Universidad 3004, Copilco Universidad, Coyoacán, 04510, Ciudad de México, CDMX, Mexico.
Miguel AltamiranoPrograma de Investigación en Cambio Climático, Universidad Nacional Autónoma de México, Av. Universidad 3004, Copilco Universidad, Coyoacán, 04510, Ciudad de México, CDMX, Mexico.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper we present GeoMIP-pattern, the first global geoengineering pattern scaling dataset. This dataset is useful to generate custom solar radiation modification scenarios and to emulate the GeoMIP model output with low data volume. Temperature, precipitation, and relative humidity patterns are derived from ScenarioMIP SSP5-8.5 and GeoMIP G6sulfur model output data using a two-step approach: the first scaling patterns are obtained by estimating a linear regression between the field of interest and the SSP5-8.5 annual global mean surface temperature, allowing for the calculation of no stratospheric aerosol injection scenarios for a global spatial grid. The second patterns are produced by regressing the field of interest on the annual global temperature difference between the G6sulfur and SSP5-8.5 experiments, which in combination with the first pattern emulates the stratospheric aerosol injection scenario. The dataset contains validation statistics, including unit root hypothesis tests novel to the field of pattern scaling, which demonstrate that the pattern slope adequately captures local changes, with most regions showing no remaining trend or nonstationarities in the residuals.

Identifiers

PMID40750609
PMCPMC12316978

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.