# Local hourly trends in near-surface and land surface temperatures

Sarah Safieddine, Cathy Clerbaux, Joaquín Muñoz-Sabater, Jean-Noël Thépaut

Thépaut

► To cite this version:

Sarah Safieddine, Cathy Clerbaux, Joaquín Muñoz-Sabater, Jean-Noël Thépaut. Local hourly trends in near-surface and land surface temperatures. Scientific Reports, 2025, 15 (1), pp.Article number: 29915. 10.1038/s41598-025-15731-0 . hal-05237167

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# Local hourly trends in near-surface and land surface temperatures

Sarah Safeddine<fnref n="1" />✉, Cathy Clerbaux<fnref n="1" />, Joaquín Muñoz-Sabater² & Jean-Noël Thépaut²

Essential Climate Variables, such as near-surface (T2m) and land surface temperatures (LST), are typically reported in Coordinated Universal Time (UTC) for global consistency. However, their diurnal variability leads to temperature trends that differ by the local hour, a factor not analyzed on the global nor regional scale. Using ECMWF ERA5-Land reanalysis data (1981–2022), we assess temperature trends by local hour and month. Our results show that the trends can change significantly during the day. LST and T2m warming or cooling trends peak in the afternoon, while showing large spatial variability across both hemispheres. Using MODIS observations, we show how the nominal Equator crossing times of TERRA and AQUA influence LST trends. These findings highlight the necessity of accounting for local time in climate assessments to improve adaptation strategies.

Global surface temperatures have risen by 1.2 °C to 1.5 °C above pre-industrial levels<fnref n="1" />,². While global temperature trends provide a general picture, there have been significant regional variations in temperature changes over the past few decades. High northern latitudes, especially the Arctic, are warming nearly four times faster than the rest of the globe since 1979³ due to a phenomenon known as Arctic amplification. Additionally, From 1850 to 1900 to 2011–2020, land warmed by 1.59 °C and oceans by 0.88 °C, confirming that land areas have warmed nearly twice as much as oceans⁴,⁵. In terms of seasonal variability of climate change, greater warming in spring has been observed in most Northern Hemisphere ecosystems and the typical phenological cycle of plants has shifted/advanced due to the increased warming⁶,⁷.

Concerning the diurnal variability, little is known about the climate's evolution depending on the hour of the day. Diurnal asymmetry in temperature has been observed, with night-time temperatures rising faster than daytime temperatures reducing the diel temperature range (DTR) (maximun – minimun temperature recorded daily)⁸⁻¹⁰. However, this asymmetry/changes in DTR are complex and vary regionally⁸ and recent studies suggests a reversed trend¹¹. Warming asymmetry leads to important consequences on plant phenology¹² and is largely linked to increased cloud cover¹²⁻¹⁴, soil moisture, precipitation, boundary layer depth, and local land use changes¹³,¹⁵,¹⁶.

With this complexity in mind, and instead of studying the daily minimum and maximum temperatures regionally or globally, a simple straightforward way is to study the temperature trends per hour of the day, in local time.

Meteorological agencies rely on Coordinated Universal Time (UTC) as a standard reference to synchronize data collection, weather observations, and reporting across different countries and regions. Local time, on the other hand, generally follows time zones that cover specific degrees of longitude (usually 15° wide) and are adjusted based on a central meridian. Here, we study the climate change signature at different hours of the day, in local time. We use long, robust time series of hourly temperatures from a dynamical reanalysis to evaluate how climate change evolves over land in local time. Using the European Centre for Medium-Range Weather Forecasts (ECMWF)'s latest reanalysis dataset over land, ERA5-Land¹⁷ we selected the land surface temperature (LST) and the near surface temperature (T2m) products, converting the time dimension from UTC to local time based on longitude. Our analysis covers the period 1981-2022 and does not account for daylight saving time. Additionally, some locations (e.g., India, which is UTC+5.5) are approximated using UTC+5 or UTC+6 depending on their longitudes. We also use auxiliary data such as those from GISTEMP (supplementary materials) and thermal infrared sensors on board polar orbiting satellites to complete our analysis.

## Results

Figure 1 illustrates the diurnal variation of temperature in both local time and Coordinated Universal Time (UTC) across the globe, for January (panel a) and June (panel b), averaged over the period 1981–2022. The spatial snapshot is captured at 15:00 UTC and 15:00 local time. This time was chosen because it is when the difference between UTC and local time is noticeable for temperatures, making the contrast visually clear in Fig. 1.

<footnote n="1">¹LATMOS/IPSL, Sorbonne Université, UVSQ, CNRS, Paris, France. ²European Centre for Medium-range Weather Forecasts, Reading, UK. ✉email: sarah.safeddine@latmos.ipsl.fr</footnote>
<figure><img src="image_3.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>(b)</figcaption></figure>

**Fig. 1.** Left panels: Climatological spatial distribution of the land surface temperature (LST), near-surface air temperature (T2m), and their difference for January (top) and July (bottom), averaged over the period 1981-2022. Data are shown for 15:00 in both Universal Coordinated Time (UTC) and Local Time (LT). Right panels: Global average LST, T2m, and their difference across the diurnal cycle, plotted by hour in UTC (blue) and LT (red) for January and July. The figure illustrates the diurnal variation in temperature, with LT values exhibiting a more pronounced daily cycle. The figures were created using MATLAB R2023b (www.mathworks.com/).
In the Northern Hemisphere (NH), where land mass is more extensive compared to the Southern Hemisphere (SH), the average global temperatures displayed are warmer in July than in January. In local time, the diurnal temperature cycle is more pronounced, with minimum temperatures occurring around the early morning hours and maximum temperatures, in the afternoon, around 15:00, for both LST and T2m. However, in UTC the diurnal cycle is flatter, as it is the average of many time zones at different local times, with the minimum occurring around midnight and the maximum around noon.

The difference between LST and T2m is more noticeable when observed in local time (lower subpanels in (a) and (b)). This difference varies throughout the day, generally being positive during the daytime: sunlight heats the land surface, and land has a lower heat capacity than the air above it. Consequently, LST tends to rise more rapidly during the day, amplifying the LST-T2m difference. This can be seen on the lower right subpanels in (a) and (b). Figure 1 represent a global average. Regionally, the timing of LST and T2m maxima can differ, and the resulting temperature differences can vary accordingly—often exceeding 10 °C—and tend to increase toward lower latitudes with higher insolation¹⁸.

We note that across Antarctica, ERA5 performs generally well¹⁹, but tends to underestimate near-surface temperature inversions, particularly under strong stable boundary layer conditions, due to coarse vertical resolution and limitations in surface and turbulence parameterizations²⁰,²¹.

In order to investigate the diurnal evolution of trends, we show in Fig. 2 hourly snapshots of the LST (left panels) and T2m (right panels) total trends (see Methods). The term “total trends” refer to the warming/cooling rate over 42 years (1981 to 2022). From this figure onwards, all the discussion of the temperatures and their trends refers to those in local time. For the visibility of the figure, we display the trends every 6 h, that is at 3:00, 9:00, 15:00, and 21:00.

The spatial distribution of the LST and T2m trends, at different times of the day, is very coherent and similar. The pronounced warming in the NH compared to the SH, as documented in recent decades² is evident for both LST and T2m in this figure. The datasets show mostly positive trends with strong warming in the Arctic²²,²³, the west coast of the United States²⁴, the Middle East²⁵ and South America²⁶.

Generally, LST trends are stronger than those for T2m. LST trends are also more variable than T2m trends from one hour to the other. For example, we note, for LST in India, parts of the south of the African continent and the north/north western United States (the Northern Great Plains) and parts of Canada, the trends shift from close to zero (e.g. at 3:00) to negative, 12 h later (at 15:00). For India, this can be attributed to increasing aerosol emissions²⁷,²⁸, supported by increasing aerosol optical depths retrievals from satellites²⁹. The increase in aerosol load can exert a cooling influence that offsets part of the greenhouse gas warming. In Africa and in the American continent, the literature review is limited at different hours of the day. The cooling trends in Africa can be attributed to factors such as increased vegetation cover and changes in land use³⁰. In the American continent, negative trends in soil temperatures have been observed in some stations in southern Canada in spring and summer³¹. More research is therefore needed to understand the diurnal change in temperature trends in these regions.

The T2m trends are hard to distinguish from one hour to the other. In order to see how the trends evolve over the day, we show in the lower panel of Fig. 2 the difference in trends between two hours shown on the upper panels, 15:00 and 3:00. We can see that both LST and T2m trends change clearly within 12 h. We note a spatial mismatch in trends in Antarctica due to the way we rearranged trends in local time per timezone (noticed by the different vertical lines).

While the 12 h difference is larger for LST, both LST and T2m show similar spatial features:

* Increase in trends during daytime in most of Europe, South America and Asia, except for South East Asia.

* Decrease in trends during daytime in arid regions in the Middle East and North Africa, as well as in the regions discussed above (South East Asia, in particular India, parts of south African continent, and south west Canada).

The larger negative difference in T2m trends compared to LST over the Arabian Peninsula likely reflects the stronger influence of urban cooling during the day over this part of the world³² and delayed nocturnal heat retention in near-surface air, particularly in arid urban environments. While LST responds more directly and rapidly to surface radiative conditions, T2m integrates effects of urban morphology, heat capacity, and atmospheric mixing, which enhance the diurnal asymmetry in temperature trends. This leads to a stronger apparent cooling in daytime T2m relative to nighttime, more so than is captured by LST.

To analyse how hourly trends vary by month, Fig. 3 presents the total trends of LST, T2m and their differences [LST - T2m] for the Northern and Southern Hemispheres, separated by the month of the year over the period 1981–2022. Each of the panels has a different y axis limit, in order to highlight its diurnal variability.

We note that the highest trends are recorded for both LST and T2m in March in the NH, and in October in the SH (spring season of both hemispheres). Moreover, fall in the NH (October and November), as well as the rest of the spring months (April and May) show trends above the average for both LST and T2m. We compare those trends/validate them against the GISS Surface Temperature Analysis version 4 (GISTEMP v4) in Supplementary Materials, Figure S1. GISTEMP is an estimate of global T2m evolution and the T2m trends calculated and shown in Fig. 3 agree well with the monthly averaged trends from GISTEMP, with highest trends recorded in spring (and in fall in the NH).

The diurnal variation of the trends is much more pronounced for LST than for T2m. In fact, when examining the trends by local hour in Figs. 2 and 3, we observe that globally, the highest LST trends occur predominantly during the day. However, the timing of maximum T2m trends varies depending on the month and hemisphere, exhibiting a generally flatter shape (black solid line in Fig. 3). In particular, we note that in the SH, the highest trends occur at around 18:00.
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** (a) Total trends in local time over the period 1981 to 2022, for land surface temperature (LST, left) and near surface temperature (T2m, right). For clarity the trends are shown every 6 h. (b): difference in the these trends between 15:00 and 3:00, to highlight the diurnal variability of the trends of LST and T2m. The figures were created using MATLAB R2023b (www.mathworks.com/).</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Monthly and hourly total trends over the period 1981–2022 (42 years) for Land Surface Temperature (LST) and near surface Temperature (T2m), along with their differences. The black solid line is the mean trend over all months. Note that the y axis limit is different in each of the panels, in order to highlight the diurnal pattern of trends.</figcaption></figure>

In the NH, the maximum trends in LST typically occur during daytime, for example at 11:00 in November and between 13:00 and 15:00 for the rest of the year, while in the SH, the maximum LST trends generally appear between 16:00 and 17:00, except in January and February. For T2m, the trend maxima are generally higher during daytime. However, in some months of the year, e.g., April to September in the NH, we note that the trends exhibit a first peak in the morning, around 9:00 and second peak 12 h later, around 21:00.

As an example, the LST and T2m trends at 9:00, 15:00, and 21:00 during January, March, May, July, September and November are shown in Supplementary material Figs. 2 and 3 respectively. The figures suggest that averaging trends across an entire hemisphere masks spatial variability, as each hemisphere encompasses different land cover types and near surface processes. For example, due to the Arctic Amplification, temperature have risen faster there than the global average³³. However, localized winter cooling trends have been observed in parts of Russia and Central Eurasia³⁴ and this is seen at different times of the day in January in the SI figures S2 and S3 for T2m and LST respectively. In March and May in the NH, and September in the SH, the highest temperature trends (in absolute value) are recorded, peaking at 15:00 for both LST and, to a lesser extent, T2m at the same location. That means that the total warming or the cooling rate becomes stronger at 15:00; a result also seen here in Fig. 2.

We note that the LST – T2m trends which are shown in Fig. 3 show a clear diurnal variability. The figure shows that land is heating at faster rate than the near surface air³⁵ and that this is particularly true and more intense during daytime.

In order to check the ERA5-Land LST trends with real observations, we study those inferred from thermal infrared (TIR) sounders, particularly those situated on Low Earth Orbit (LEO) polar orbiting platforms. The LEO polar orbit configuration enables satellites to observe the entire globe twice a day, typically at 12-hour intervals, and they pass at the same local time each day. For this, we choose LST collected by the MODIS instruments aboard NASA's Terra and Aqua satellites, with crossing times at 10:30 am/pm and 1:30 am/pm local time respectively. The full common years for Terra and Aqua begin in 2003. Figure 4 shows the progression of the total trends over the period 2003–2022 (20 years) in LST, alternating between Terra and Aqua to highlight the diurnal evolution. Additionally, to the satellite trends, the ERA5-Land trends closest in time (at 02:00, 11:00, 14:00 and 23:00) are also presented. The 01:00, 10:00, 13:00 and 22:00 ERA5-Land trends show similar results and are not shown here.

Generally, satellite and ERA5-Land trends data are consistent, and follow the diurnal pattern presented in Fig. 3 (although for a shorter period), with higher trends in daytime at 10:30/11:00 and 13:30/14:00 than those in the evening orbits, 12 h later.
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** (a) Total LST trends over the period [2003–2022] for MODIS on board the two polar orbiting satellites, AQUA and TERRA, (b) total trends over the same period for ERA5-Land, 30 min after the satellite crossing time. The average LST trend for both hemispheres over the entire period is included in the figures. The figures were created using MATLAB R2023b (www.mathworks.com/).</figcaption></figure>

As in Fig. 2, for the satellite data, the Arctic is heating faster than the rest of the world, India is showing even stronger negative trends in the past two decades particularly during the day. This cooling trend is also seen in central and east Africa. In warm tropical environments, a study³⁶ reported an increase in the frequency and intensity of extreme rainfall events. In East Africa, drought under different climate scenarios follow the
paradigm “dry get drier and wet gets wetter”. This means that dry regions (already low in rainfall) are projected to experience even less precipitation, worsening drought conditions, and increasing temperatures as seen in the positive Tskin trends Northern Africa. On the other hand, wet regions (with higher rainfall) are expected to receive more precipitation, increasing the risk of floods or excessive soil moisture, with decreasing skin temperatures particularly recorded in Kenya, Uganda, and Ethiopian highlands³⁷,³⁸. The LST trends in Australia, particularly negative ones in New South Wales (NSW), and positive ones in the North of the continent need to be investigated in more detail because they cannot be explained by rainfall trends/cloud cover as in general, the North is getting wetter and NSW is getting drier³⁹. However, the algorithm used here for MODIS LST retrievals⁴⁰ does not incorporate explicit corrections for aerosol effects. Aerosol presence can introduce biases in LST measurements⁴¹ and therefore can affect the trends shown here.

In Supplementary material Figure S4, we show the total LST trends over the period 2004 to 2022 (19 years) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the geostationary Meteosat Second Generation (MSG), with observations covering Africa, most of Europe, and part of South America⁴²,⁴³. Trends are shown at 2:00, 11:00, 14:00 and 23:00 local time. Trends over Africa and Europe have a clear diurnal variability with the highest trends at 11:00 and 14:00, in agreement with what is shown in Fig. 4. SEVIRI's trends are generally higher in magnitude, in agreement with the literature that shows an overestimation of LST from SEVIRI with respect to MODIS or ERA5-Land⁴⁴⁻⁴⁷. The average NH and SH trends are included on Fig. 4. MODIS reveals that the NH landmass is warming at a rate at least 2 to 4 times faster than that of the SH. While the spatial trends appear to have higher magnitudes during daytime, the numbers on Fig. 4 suggest the opposite. This is driven by the cooling observed over India (see discussion of Figs. 2 and 3) which offsets the positive hemispheric trend. Satellite LST responds more immediately and strongly to radiative changes (like reduced incoming or outgoing radiation) while the air temperature responds more slowly, buffered by the heat capacity of the air and continued heat fluxes from the sub-surface and surroundings. Over long periods this induces stronger LST trends.

## Discussion and conclusions

The climate change signature exhibits spatial and temporal variability across multiple timescales. In this study, we investigate the spatial and diurnal variability in LST and T2m trends. Meteorological organizations rely on the Universal Coordinated Time for data delivery. Here, we study for each location on Earth the diurnal temperature and its trends in local time, depending on longitude-based time zones. As such, this work investigates the climate change response for each local hour of the day over the period from January 1981 to December 2022, for LST, T2m, and the difference between the two. Our results show that Temperature trends for both LST and T2m are larger in spring in both hemispheres, and are also high in fall in the NH and that the LST maximum temperature trends in warming or cooling occur typically in the afternoon.

Averaged ERA5-Land and MODIS LST trends show overall a good spatial agreement, however, some differences can be observed due to the intrinsic particularities of satellite retrievals and reanalysis. Ground-based observational data are limited in many SH regions. This may reduce the accuracy of the reanalysis in those areas compared to the NH. Moreover, LST is a diagnostic (non-prognostic) variable of ERA5-Land that is calculated at each time step based on surface energy balance equations. Since LST is diagnostic, it is not a primary variable directly predicted by the model (unlike near surface temperature). Having said that, the LST long term trends analyzed in Figs. 2 and 3 are hard to validate, and while MODIS provides valuable insights into recent surface temperature trends, the longer temporal coverage of ERA5-Land analyzed in Figs. 2 and 3 allows for the identification of multi-decadal climate patterns and their evolution, which are critical for understanding the full scope of climate change at different times of the day.

Moreover, and in contrast to MODIS, which may have data gaps or inconsistencies due to cloud cover or sensor limitations, ERA5-Land provides a seamless record of LST across all regions and time periods. Despite discrepancies in short-term trends, the unparalleled temporal coverage, global consistency, and robust integration of observations make ERA5-Land a very good tool for long-term climate analysis at different hours of the day.

Our findings suggest that climate change impacts vary by time of day, and thus, adaptation strategies should consider local time. This has practical implications for future agricultural practices, scheduling of outdoor sports and athletic events, and the design of climate control systems in urban environments.

## Methods

### ERA5-Land data: land surface and near surface temperatures

Framed within the Copernicus Climate Change Service (C3S) of the European Commission, the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5-Land reanalysis¹⁷ is an enhanced global dataset of the land component of the fifth generation of European ReAnalysis (ERA5). From this dataset, we extract land surface temperature (LST) and near surface temperatures (T2m). ERA5-Land data are available at 0.1° × 0.1° resolution (approximately 9 km horizontal resolution).

ERA5-Land has been shown to capture the diurnal cycle of T2m and LST with generally good accuracy across most land regions¹⁷. Nonetheless, regional biases persist, and daytime LST tends to be underestimated, especially in arid and semi-arid regions in summer⁴⁵ and in the tropics⁴⁸ where fine-scale land-atmosphere interactions and radiative processes are not fully resolved.

We compute hourly averages for each month of the year over the study period, which is between 1981 and 2022. This creates a matrix of 24 h x 12 months x 42 years for each latitude and longitude on Earth, corresponding to the hourly average temperatures for each month of the year, over 42 years. The 24-hour dimension is then adjusted by 15° longitude bins to convert to local time. All averages shown (global, hemispheric) are weighted per grid area.
## MODIS LST temperature products

The Land Surface Temperature product is the 6.1 version, developed by NASA's Land Processes Distributed Active Archive Center (LP DAAC), which provides monthly Land Surface Temperature and Emissivity monthly data derived from the Terra (Wan et al., 2021) and Aqua (Wan et al., 2021a) Moderate Resolution Imaging Spectroradiometer (MODIS). These data are presented in a 0.05-degree latitude/longitude grid. NASA's Terra is a sun-synchronous, polar orbiting satellite in a morning orbit, with equator crossing times of approximately 10:30 am and 10:30 pm (local time) in descending and ascending modes respectively. The Aqua satellite is also a polar orbiting satellite but in an early afternoon orbit, with equator crossing tiles of approximately 01:30 am and 01:30 pm (local time) in descending and ascending modes respectively.

Terra and Aqua have been drifting to an earlier equatorial crossing time since 2020/2021. By end of 2022, Terra's and Aqua's crossing time drifted by around 12 to 20 min, for Aqua and Terra respectively (e.g. https://aqu.a.nasa.gov/). We do not expect that the orbital drift to significantly impact the LST trends shown here.

## Trend analysis

To calculate the LST and T2m evolution over the study period, we label these as "total trend", corresponding to the temperature increase or decrease per grid area over the study period, which is 42 years for the ERA5-Land dataset, and 20 years for MODIS. These correspond to the sum of the linear trends per year for each hour, and for the different data series. The linear trends are calculated by least squares analysis using the Climate Data Toolbox for MATLAB⁵¹ and follow the method used in the GISS Surface Temperature Analysis⁵².

## Data availability

ERA5-Land hourly data from 1981 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) at: https://doi.org/10.24381/cds.e2161bacGISS Surface Temperature Analysis (GISTEMP), version 4 are obtained NASA Goddard Institute for Space Studies at data.giss.nasa.gov/gistemp/ The Modis TERRA MOD11C3 Version 6.1 data can be accessed through NASA's LP DAAC data portal: https://lpdaac.usgs.gov/products/mod11c3v061/The Modis AQUA MYD11C3 Version 6.1 data can be accessed through NASA's LP DAAC data portal: https://lpdaac.usgs.gov/products/myd11c3v061/.SEVIRI LST product is part of the Land Surface Analysis (LSA SAF) project at EUMETSAT accessible here: https://datalsasaf.lsamvcs.ipma.pt/PRODUCTS/MSG/MLST/NETCDF/.

Received: 2 April 2025; Accepted: 11 August 2025

Published online: 15 August 2025

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## Acknowledgements

This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 and innovation programme (Grant Agreement No. 742909). The authors thank Hugo Panchaud for his help for the extraction of the MODIS and SEVIRI data.

## Author contributions

S.S. wrote the main manuscript under the grant from C.C. C.C., J.N.T, and J.M.S. provided scientific feedback and all authors reviewed the manuscript.

## Competing interests

The authors declare no competing interests.

## Additional information

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1038/s41598-025-15731-0.

**Correspondence** and requests for materials should be addressed to S.S.

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