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Cited by 12Climate variability and modelsClimate Change and Environmental ImpactHydrology and Drought Analysis

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Climate Change and Extreme Events in Northeast Atlantic and Azores Islands Region

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Abstract

In small island regions, the influence of climate change assumes particular relevance. In the Azores archipelago, made up of nine islands, the geographical circumstances, oceanic condition, territorial dispersion, land use model and other physiographic constraints reinforce and enhance the vulnerability of the islands to changes in current weather patterns. Coupled Model Intercomparison Phase 6 (CMIP6) projections are used for the northeast Atlantic region to evaluate daily extreme climate events in large scale for the Azores region. Results shows changes in the annual maximum number of consecutive dry days, the annual number of wet days, and especially in the annual number of tropical nights. Despite limitations due to the lack of spatial detail, the large-scale framework suggests changes that may be enhanced by topography, particularly with respect to precipitation. The conclusions point to the need to establish standard rules in the processes of design, reviewing and/or amending territorial management instruments at the municipal scale in the Autonomous Region of the Azores, with the goal of adapting to a different climate from the recent past.

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Overview

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Written by AI from the paper’s full text.

TL;DR

This study uses CMIP6 projections to assess daily extreme climate indices in the Azores and northeast Atlantic, finding significant warming and increases in tropical nights, while precipitation changes remain small and scenario-dependent. It concludes that territorial management in the Azores should adapt to a climate different from the recent past, but coarse model resolution limits island-scale inference.

Key findings

  • Atmospheric CO₂ at Serreta increased significantly over 1980–2022 (measurements 1.73 ± 0.03 ppm/year; historical simulation 1.85 ± 0.03 ppm/year). Scenarios diverge after about 2024; SSP2-4.5 tends toward ~600 ppm(v), SSP1-2.6 reaches a maximum by 2060, and SSP5-8.5 increases exponentially.
  • CMIP6 historical daily minimum temperature underestimates ERA5 by -0.56 ± 0.27 K (p < 0.05) and shows a positive trend of 0.038 ± 0.003 K/decade from 1850–2014. Projected trends are 0.055 ± 0.006, 0.118 ± 0.004, and 0.225 ± 0.003 K/decade for SSP1-2.6, SSP2-4.5, and SSP5-8.5; end-century changes are 0.073 ± 0.289 K (not significant), 0.539 ± 0.293 K, and 1.221 ± 0.347 K.
  • Tropical nights show a historical bias of -26.0 ± 12.2 nights. Projected trends are 0.20 ± 0.02, 0.38 ± 0.01, and 0.72 ± 0.01 days/decade; end-century changes are -7.57 ± 12.57 days (SSP1-2.6), 6.71 ± 12.03 days (SSP2-4.5), and 28.15 ± 12.56 days (SSP5-8.5). The ensemble range can vary from zero to almost 300 days by the end of the century.
  • Annual precipitation historical bias is -6.3 ± 123.7 mm (p = 0.78, not significant), and projected annual precipitation changes are not statistically significant for the Azores in all scenarios. CDD historical bias is 0.61 ± 5.3 days (not significant), with projected changes of 2.83 ± 5.20, 3.78 ± 5.01, and 5.64 ± 4.96 days. R20 mm historical bias is -0.16 ± 1.80 days (not significant); positive significant trends occur in all scenarios, with SSP2-4.5 and SSP5-8.5 end-century changes of 1.1 ± 1.9 days.

Methods

  • The study defines an Azores region between 36.5–40°N and 32–24.5°W, plus a larger northeast Atlantic area (30–50°N, 5–45°W). It uses 33 CMIP6 models, with subsets depending on variable and scenario, only variant r1i1p1f1, and three SSP scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5.
  • Daily total precipitation (PRECTOT) and daily minimum temperature (TNM) are analyzed. Extreme indices CDD, R20 mm, and TR are computed with XCLIM. ERA5 1961–1990 is the reference, regridded to 0.25° × 0.25°. Spatial averages, biases, linear trends, and projected changes for 2071–2100 relative to 1961–1990 are evaluated with t-tests at p < 0.05. CO₂ data come from Serreta station (1980–2022) and input4MIPs historical/projection datasets.

Limitations

  • Paper-stated: CMIP6 projections reflect large-scale features, not each island individually; island-scale differences may depend on physiographic characteristics. Ensemble means smooth year-to-year amplitudes, so individual years should be treated cautiously. The methodology only allows annual trends and changes, not seasonal trends. The authors also note no comparable literature evaluations of CMIP6 or CMIP5 extremes in oceanic regions or small islands.
  • Paper-stated resolution limits: ERA5 resolution is about 28 km latitude × 22 km longitude; only São Miguel, Pico, Terceira, and São Jorge are larger than this. ERA5 underestimates topography (e.g., highest elevation in ERA5 for the Azores region is 116 m on São Miguel versus 1103 m actual; Pico shows 80 m in ERA5 versus 2351 m actual). CMIP6 nominal resolution is 100–500 km, while São Miguel is only 64 km long; most models show maximum altitudes of 3–27 m, some show no elevation, and all underestimate ERA5 by at least 100 m.
  • Evident from the text: several sections and tables are duplicated or truncated in the provided full text, which limits verification of some reported values and reproducibility.

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Integrity signals

Registry

Crossref · 10.3390/cli11120238

Cites

17 of 18 references verified0 citation errors · 0 not checkable

Statistics

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Figures

Figure 1 Map of the Eastern North Atlantic with the location of Azores archipelago. The highlighted rectangle corresponds to the working area (36.5° N-40° N and 32° W-24.5° W). The mesh represents the spatial resolution used in this work (0.25° × 0.25°). Figure 1. Map of the Eastern North Atlantic with the location of Azores archipelago. The highlighted rectangle corresponds to the working area (36.5 ◦ N-40 ◦ Nand 32 ◦ W-24.5 ◦ W). The mesh represents the spatial resolution used in this work (0.25 ◦ × 0.25 ◦ ).
Figure 1 Map of the Eastern North Atlantic with the location of Azores archipelago. The highlighted rectangle corresponds to the working area (36.5° N-40° N and 32° W-24.5° W). The mesh represents the spatial resolution used in this work (0.25° × 0.25°). Figure 1. Map of the Eastern North Atlantic with the location of Azores archipelago. The highlighted rectangle corresponds to the working area (36.5 ◦ N-40 ◦ Nand 32 ◦ W-24.5 ◦ W). The mesh represents the spatial resolution used in this work (0.25 ◦ × 0.25 ◦ ).CC-BY · From the PDF
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Figure 4 Annual mean of daily minimum temperature. Left : Time series of CMIP6 ensemble mean and range for the Azores region (1850-2014). Right : Map of historical CMPI6 ensemble mean bias relative to ERA5 (1961-1990). Hatched areas represent statistically significant ( p -value < 0.05) bias. Figure 4. Annual mean of daily minimum temperature. Left : Time series of CMIP6 ensemble mean and range for the Azores region (1850-2014). Right : Map of historical CMPI6 ensemble mean bias relative to ERA5 (1961-1990). Hatched areas represent statistically significant ( p -value < 0.05) bias.
Figure 4 Annual mean of daily minimum temperature. Left : Time series of CMIP6 ensemble mean and range for the Azores region (1850-2014). Right : Map of historical CMPI6 ensemble mean bias relative to ERA5 (1961-1990). Hatched areas represent statistically significant ( p -value < 0.05) bias. Figure 4. Annual mean of daily minimum temperature. Left : Time series of CMIP6 ensemble mean and range for the Azores region (1850-2014). Right : Map of historical CMPI6 ensemble mean bias relative to ERA5 (1961-1990). Hatched areas represent statistically significant ( p -value < 0.05) bias.CC-BY · From the PDF
of mean and amplitude ranges of the ensembles in all scenarios, especially in SSP2 4.5 and SSP5 8.5 (Table 5). ssp245 0.118 ± 0.004 5.14 × 10 -47 0.539 ± 0.293 1.08 × 10 -14 ssp585 0.225 ± 0.003 1.91 × 10 -79 1.221 ± 0.347 2.01 × 10 -27
of mean and amplitude ranges of the ensembles in all scenarios, especially in SSP2 4.5 and SSP5 8.5 (Table 5). ssp245 0.118 ± 0.004 5.14 × 10 -47 0.539 ± 0.293 1.08 × 10 -14 ssp585 0.225 ± 0.003 1.91 × 10 -79 1.221 ± 0.347 2.01 × 10 -27CC-BY · From the PDF
Figure 6 Simulations of annual number of tropical nights for the Azores region (1961-1990) computed from ERA5 hourly temperatures and CMIP6 historical daily minimum temperatures. Figure 6. Simulations of annual number of tropical nights for the Azores region (1961-1990) computed from ERA5 hourly temperatures and CMIP6 historical daily minimum temperatures.
Figure 6 Simulations of annual number of tropical nights for the Azores region (1961-1990) computed from ERA5 hourly temperatures and CMIP6 historical daily minimum temperatures. Figure 6. Simulations of annual number of tropical nights for the Azores region (1961-1990) computed from ERA5 hourly temperatures and CMIP6 historical daily minimum temperatures.CC-BY · From the PDF
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CC-BY · From the PDF
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Image 9
CC-BY · From the PDF
Figure 10 CMIP6-projected annual precipitation for the Azores region and for SSP1 2.6 ( top ), SSP2 4.5 ( middle ) and SSP5 5.8 ( bo tt om ) scenarios. Left : Ensemble mean and range time series. Right : Map of projected ensemble mean changes for 2071-2100 relative to ERA5 1961-1990 reanalysis (hatched areas represent statistically significant changes, p -value < 0.05). Figure 10. CMIP6-projected annual precipitation for the Azores region and for SSP1 2.6 ( top ), SSP2 4.5 ( middle ) and SSP5 5.8 ( bottom ) scenarios. Left : Ensemble mean and range time series. Right : Map of projected ensemble mean changes for 2071-2100 relative to ERA5 1961-1990 reanalysis (hatched areas represent statistically significant changes, p -value < 0.05).
Figure 10 CMIP6-projected annual precipitation for the Azores region and for SSP1 2.6 ( top ), SSP2 4.5 ( middle ) and SSP5 5.8 ( bo tt om ) scenarios. Left : Ensemble mean and range time series. Right : Map of projected ensemble mean changes for 2071-2100 relative to ERA5 1961-1990 reanalysis (hatched areas represent statistically significant changes, p -value < 0.05). Figure 10. CMIP6-projected annual precipitation for the Azores region and for SSP1 2.6 ( top ), SSP2 4.5 ( middle ) and SSP5 5.8 ( bottom ) scenarios. Left : Ensemble mean and range time series. Right : Map of projected ensemble mean changes for 2071-2100 relative to ERA5 1961-1990 reanalysis (hatched areas represent statistically significant changes, p -value < 0.05).CC-BY · From the PDF
Figure 11 Simulations of annual number of consecutive dry days for the Azores region (1961-1990 computed from ERA5 hourly precipitation amounts and CMIP6 historical daily precipitatio Figure 11. Simulations of annual number of consecutive dry days for the Azores region (1961-1990) computed from ERA5 hourly precipitation amounts and CMIP6 historical daily precipitation amounts.
Figure 11 Simulations of annual number of consecutive dry days for the Azores region (1961-1990 computed from ERA5 hourly precipitation amounts and CMIP6 historical daily precipitatio Figure 11. Simulations of annual number of consecutive dry days for the Azores region (1961-1990) computed from ERA5 hourly precipitation amounts and CMIP6 historical daily precipitation amounts.CC-BY · From the PDF
Image 12
CC-BY · From the PDF
Image 13
CC-BY · From the PDF
Figure 14 CMIP6-projected annual number of wet days for the Azores region and for SSP1 2.6 ( top ), SSP2 4.5 ( middle ) and SSP5 5.8 ( bottom ) scenarios. Left : Ensemble mean and range time series. Right : Map of projected ensemble mean changes for 2071-2100 relative to ERA5 1961-1990 reanalysis (hatched areas represent statistically significant changes, p -value < 0.05).
Figure 14 CMIP6-projected annual number of wet days for the Azores region and for SSP1 2.6 ( top ), SSP2 4.5 ( middle ) and SSP5 5.8 ( bottom ) scenarios. Left : Ensemble mean and range time series. Right : Map of projected ensemble mean changes for 2071-2100 relative to ERA5 1961-1990 reanalysis (hatched areas represent statistically significant changes, p -value < 0.05).CC-BY · From the PDF

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Citations

Cites

Show all 17 references

Research works printed but not linked: 10

  1. (2022). Global Warming of 1.5 °C, Cambridge University Press.
  2. Wallace, J.M., and Hobbs, P.V. (2006). Atmospheric Science: An Introductory Survey: Second Edition, Elsevier Inc.
  3. Stensrud, D.J. (2009). Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models, Cambridge University Press.
  4. Carvalho, 2020, Alterações Climáticas e o Aumento de Eventos Extremos Nos Açores, Bol. Núcleo Cult. Horta, 29, 95
  5. Carvalho, 2020, Alterações Climáticas e Energia No Contexto Dos Açores, Bol. Núcleo Cult. Horta, 29, 95
  6. Carvalho, F.R.S., Meirelles, M.G., Henriques, D.V., Navarro, P.V., and Vasconcelos, H.C. (2022). Climate Change Management, Springer Science and Business Media.
  7. Mcavaney, B.J., and Holland, G.J. (1995). World Survey of Climatology, Elsevier.
  8. Sandstad, M., Schwingshackl, C., and Iles, C. (2022, June 01). Climate Extreme Indices and Heat Stress Indicators Derived from CMIP6 Global Climate Projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). Available online: https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.776e08bd?tab=overview. · DOI
  9. Pörtner, H.-O., Roberts, D.C., Tignor, M., Poloczanska, E.S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., and Möller, V. (2022). Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press.
  10. Margolis, H.G. (2014). Global Climate Change and Public Health, Springer.

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Metadata

Authors
Carvalho, F. S., Meirelles, M. G., Henriques, D., Porteiro, J., Navarro, P., & Vasconcelos, H. C.
Affiliations
  • Portuguese Sea and Atmosphere Institute (PT)
  • Universidade dos Açores (PT)
Corresponding author
Vasconcelos, Helena Cristina
Venue
Climate
Publisher
MDPI AG
Published
2023-12-04
Volume
11
Issue
12
Pages
238
ISSN
2225-1154 (ISSN-L)
OpenAlex
W4389306455
Cited by
12
References
17
Citations by year
2026: 5 · 2025: 6 · 2024: 1
FWCI
0.94
Type
journal article
Language
en
Open access
Locations
  • Climate (publishedVersion, cc-by)
  • Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
Topics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Environmental Impact
  • Physical Sciences › Environmental Science › Global and Planetary Change › Hydrology and Drought Analysis
Keywords
climate change, climate extremes, Azores, Northeast Atlantic, CMIP6 projections, consecutive dry days, tropical nights, rainy days, precipitation, climate change adaptation, territorial management, topographic effects on precipitation

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