The influence of land surface temperature on Ghana's climate variability and implications for sustainable development

Abstract

Climate change poses significant global challenges, especially in the West African sub-region, with high temperature and precipitation patterns variability, threatening socio-economic stability and ecosystem health. While global factors such as greenhouse gases and oceanic circulations shape regional climates, this study focuses on the understudied role of local climatic variables in influencing near-surface air temperature (NST) in Ghana from 1981 to 2020. Based on ground observations, our findings reveal significant correlations between land surface temperature (LST) and NST before and after the identified breakpoint year of 2001. Additionally, we observe a reduction in precipitation post-2001. We also identify LST as the primary driver of NST and precipitation changes based on cause-effect analysis of multiple factors. Specifically, higher LST leads to decreased precipitation and increased NST, contributing to the increasing trend of NST over the last two decades. The insights are vital for developing targeted adaptation strategies, including integrated land and water management, sustainable agriculture, and effective interventions, directly supporting the United Nations Sustainable Development Goals (SDG) 13 (Climate Action) and SDG 15 (Life on Land). Moreover, the study provides evidence for promoting climate-smart agriculture to ensure food security (SDG 2). By integrating these findings into climate adaptation frameworks, policymakers and stakeholders can better address the unique challenges posed by climate variability in Ghana, ensuring more resilient and sustainable environmental management.

Keywords: climate change adaptation; land surface temperature; multivariate causality analysis; near–surface temperature; precipitation; sustainable development goals.

Fig. 1

Map of the study area. a Agroecological zones and the 16 administrative regions of Ghana a location of Ghana in Africa. The digital elevation model (DEM) dataset is obtained from the Shuttle Radar Topography Mission (SRTM), with a 90-m spatial resolution. Source: USGS Earth Explorer.

Fig. 2

Interannual variability of mean annual temperature anomaly over Ghana from 1981 to 2020 (unit: °C) based on CRU (red) and CPC (blue). Dashed lines represent linear trend lines. The anomalies are calculated relative to the 1991–2020 climate normal period.

Fig. 3

Spatial distribution of change in decadal mean temperature anomaly over Ghana from 1981–2020, relative to the 1991–2020 climate normal period. a 1981–1990, b 1991– 2000, c 2001– 2010, and d 2011– 2020.

Fig. 4

Violin plots display annual temperature anomalies distribution for 1981–2000 (blue) and 2001–2020 (red).

Fig. 5

Relationship between local climatic variables and changes in mean temperature over Ghana during the December, January, and February (DJF) season. Scatter plot of the standardized mean temperature anomalies versus the standardized mean anomalies of a land surface temperature (LST), b precipitation (PRE), c relative humidity (RH), d total cloud cover (TC). The “r” represents the correlation coefficient, and “*” symbolizes a significant correlation at a 95% confidence level. The color scheme distinguishes between two periods: BC (1981–2000, blue) and AC (2001–2020, red). The distribution of red and blue data points provide insight into how the relationships between various local climatic variables and NST anomalies have evolved.

Fig. 6

Relationship between local climatic variables and changes in mean temperature over Ghana during the June, July, and August (JJA) season. Scatter plot of the standardized mean temperature anomalies versus the standardized mean anomalies of a land surface temperature (LST), b precipitation (PRE), c relative humidity (RH), d total cloud cover (TC). The “r” represents the correlation coefficient, and “*” symbolizes a significant correlation at a 95% confidence level. The color scheme distinguishes between two periods: BC (1981–2000, blue) and AC (2001–2020, red). The distribution of red and blue data points provide insight into how the relationships between various local climatic variables and NST anomalies have evolved.

Fig. 7

Relationship between local climatic variables and changes in mean temperature over Ghana for the annual time scale. Scatter plot of the standardized mean temperature anomalies versus the standardized mean anomalies of a land surface temperature (LST), b precipitation (PRE), c relative humidity (RH), d total cloud cover (TC). The “r” represents the correlation coefficient, and “*” symbolizes a significant correlation at a 95% confidence level. The color scheme distinguishes between two periods: BC (1981–2000, blue) and AC (2001–2020, red). The distribution of red and blue data points provide insight into how the relationships between various local climatic variables and NST anomalies have evolved.

Fig. 8

Multivariate causality network among key climatic variables LST: land surface temperature, PRE: precipitation, RH: relative humidity, TC: total cloud cover, and their influence on near-surface air temperature (NST) during 1981–2000 before the change (BC). The arrows indicate the direction of information flow, with thickness visually representing the magnitude of influence between variables.

Fig. 9

Multivariate causality network among key climatic variables LST: land surface temperature, PRE: precipitation, RH: relative humidity, TC: total cloud cover, and their influence on near-surface air temperature (NST) during 2001–2020 after the change (AC). The arrows indicate the direction of information flow, with thickness visually representing the magnitude of influence between variables.