Composite of Lisbon, Oporto, and Coimbra cityscapes with rising temperature lines, symbolizing regional climate change.

Is Your City Heating Up Faster? How Climate Change Impacts Vary Regionally

Soraya Malik in Climate & Environment August 2025 • 4 min read.

"New research reveals the uneven rise in temperatures across Portuguese cities, highlighting the need for localized climate monitoring and adaptation strategies."

Global warming is a pressing issue, but its effects aren't uniform across the planet. While international agreements like the Paris Agreement set goals to limit global temperature rise, understanding the specific impacts at a local level is crucial for effective action. This means diving into regional and local data to see how climate change is playing out in individual communities.

Analyzing temperature trends at a local level allows us to avoid the uncertainties that come with broader global averages, and can provide better clarity on how regions are adapting to global warming. This is particularly relevant in areas like the Iberian Peninsula, which has experienced significant climate shifts in recent decades. By looking at specific cities and regions, researchers can pinpoint the unique challenges and opportunities they face.

A new study focuses on the long-term temperature records of three Portuguese cities: Lisbon, Oporto, and Coimbra. By applying advanced statistical methods, the research reveals distinct warming patterns in each city, highlighting the importance of localized climate analysis. The study uses a periodic mixed linear state-space model (PMLSS) to analyze monthly temperature data, offering insights into seasonal variations and long-term trends.

Decoding the Temperature Puzzle: How Warming Trends Differ Across Cities

Composite of Lisbon, Oporto, and Coimbra cityscapes with rising temperature lines, symbolizing regional climate change.

The study uses sophisticated statistical tools to dissect long-term temperature data from Lisbon, Oporto, and Coimbra. This method helps account for seasonal temperature variations and the natural correlation between temperature readings over time. By using a periodic state-space framework and a version of the Kalman filter, researchers were able to estimate monthly warming rates for each city.

The analysis revealed some striking differences. The greatest annual mean rise was found in Oporto, with a significant 2.17°C increase per century. In contrast, Lisbon and Coimbra experienced more moderate warming, with increases of 0.62°C and 0.55°C per century, respectively. These findings underscore that global warming doesn't translate to uniform temperature increases everywhere.

Here are some key highlights from the data:

Oporto: Showed the most significant annual mean temperature increase at 2.17°C per century.
Lisbon: Experienced a warming rate of 0.62°C per century.
Coimbra: Recorded a warming rate of 0.55°C per century.
Seasonal Variations: The study also identified different monthly warming rates, indicating that temperature increases vary throughout the year.

These variations emphasize the importance of not just looking at overall trends, but also understanding when and how these changes occur. For example, Oporto's higher warming rate could have specific implications for its climate and ecosystems, compared to Lisbon and Coimbra.

What Does This Mean for You?

Understanding how climate change manifests differently in various regions is crucial for developing targeted strategies. For example, cities with higher warming rates, like Oporto, may need to focus on heatwave preparedness and water conservation. Meanwhile, Lisbon and Coimbra might prioritize strategies related to shifting agricultural patterns or changing tourism seasons. By recognizing these localized impacts, communities can better prepare for the challenges and opportunities ahead. Moreover, this study underlines the importance of continuous, localized climate monitoring to inform effective policy and adaptation measures.

About this Article -

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This article is based on research published under:

DOI-LINK: 10.1002/env.2550, Alternate LINK

Title: A Periodic Mixed Linear State‐Space Model To Monthly Long‐Term Temperature Data

Subject: Ecological Modeling

Journal: Environmetrics

Publisher: Wiley

Authors: M. Costa, M. Monteiro

Published: 2018-12-03

Everything You Need To Know

What statistical methods were employed to determine the warming trends in Lisbon, Oporto, and Coimbra, and how do these methods improve the accuracy of the analysis?

The study used a periodic mixed linear state-space model (PMLSS) to analyze monthly temperature data from Lisbon, Oporto, and Coimbra. This method accounts for both seasonal temperature variations and the natural correlation between temperature readings over time. By using a periodic state-space framework and a version of the Kalman filter, researchers estimated monthly warming rates for each city, offering insights into seasonal variations and long-term trends. The strength of this method lies in its ability to dissect long-term data and pinpoint specific warming patterns.

According to this study, what are the specific annual mean temperature increases observed in Oporto, Lisbon, and Coimbra, and why is it important to note these distinctions?

The annual mean temperature rise was most significant in Oporto, with an increase of 2.17°C per century. Lisbon experienced a warming rate of 0.62°C per century, while Coimbra recorded a warming rate of 0.55°C per century. These differences emphasize that global warming doesn't result in uniform temperature increases everywhere, highlighting the importance of localized studies.

How does recognizing the varying impacts of climate change in cities like Oporto, Lisbon, and Coimbra lead to more effective adaptation strategies for these communities?

Understanding the differences in warming rates between cities like Oporto, Lisbon, and Coimbra is vital for developing targeted adaptation strategies. Oporto, with its higher warming rate, may need to prioritize heatwave preparedness and water conservation measures. Lisbon and Coimbra might focus on strategies related to shifting agricultural patterns or adapting to changing tourism seasons. This localized approach ensures that resources and policies are tailored to the specific challenges faced by each community.

Beyond temperature analysis, how does the periodic mixed linear state-space model (PMLSS) enhance the precision of climate analysis, and could this model be applied to analyze other climate variables for a more complete understanding?

The periodic mixed linear state-space model (PMLSS) enhances the precision of climate analysis by considering both seasonal temperature variations and the natural correlation between temperature readings. While this study focused on temperature, the PMLSS model could be extended to analyze other climate variables like precipitation or humidity, offering a more comprehensive understanding of regional climate changes. Applying PMLSS across a broader range of climate indicators could reveal intricate patterns and interdependencies, further refining localized climate models and adaptation strategies.

Why is continuous, localized climate monitoring essential for cities, and how does it inform more effective policy and adaptation measures, using examples from the analysis of Lisbon, Oporto, and Coimbra?

Continuous, localized climate monitoring, like that conducted in Lisbon, Oporto, and Coimbra, plays a crucial role in informing effective policy and adaptation measures. By tracking temperature trends and seasonal variations at a city level, policymakers can identify specific challenges and opportunities for their communities. Without this detailed monitoring, cities risk implementing generic strategies that may not address their unique climate vulnerabilities. Localized data allows for proactive and tailored interventions, ensuring resources are allocated efficiently and adaptation efforts are effective.