Atmospheric Reanalysis Research Articles

Removing atmospheric noise from InSAR interferograms in mountainous regions with a convolutional neural network

Atmospheric noise in interferometric synthetic aperture radar (InSAR)-derived estimates of surface deformation often obscures real displacement signals, especially in mountainous regions. As climate change disproportionately impacts the mountain cryosphere, a reliable technique for atmospheric correction in high-relief terrain is increasingly important. We developed and implemented a statistical machine learning atmospheric correction approach that relies on the differing spatial and topographic characteristics of slow-moving periglacial features and atmospheric noise. Our correction is applied at the native spatial and temporal resolution of the InSAR data, does not require external atmospheric reanalysis data, and can correct both stratified and turbulent atmospheric noise.Using Sentinel-1 data from 2017 to 2022, we trained a convolutional neural network (CNN) on observed atmospheric noise from 330 short-baseline interferograms and observed displacement signals from time series inversion of 1322 interferograms. We applied our trained CNN to correct 251 additional interferograms over an out-of-region application area, which were inverted to create displacement time series. We used the Rocky Mountains in New Mexico, Colorado, and Wyoming as our training, validation, testing, and application areas. When applied to our testing dataset, our correction offered performance improvements of 131%, 208%, and 68% in structural similarity index measure over corrections using atmospheric reanalysis data, phase correlation with topography, and high-pass filtering, respectively. The CNN-corrected time series reveals previously obscured kinematic behavior of rock glaciers and other features in the application dataset. Our flexible, robust approach can be used to correct arbitrary InSAR data to analyze subtle surface deformation signals for a range of science and engineering applications.

Evaluation of Three High-Resolution Satellite and Meteorological Reanalysis Precipitation Datasets over the Yellow River Basin in China

Evaluation of Three High-Resolution Satellite and Meteorological Reanalysis Precipitation Datasets over the Yellow River Basin in China

Evaluation of Fengyun-4B Satellite Temperature Profile Products Using Radiosonde Observations and ERA5 Reanalysis over Eastern Tibetan Plateau

The latest-generation geostationary meteorological satellite, Fengyun-4B (FY-4B), equipped with the Geostationary Interferometric Infrared Sounder (GIIRS), offers high-spatiotemporal-resolution three-dimensional temperature structures. Its deployment serves as a critical complement to atmospheric temperature profile (ATP) observation in the Tibetan Plateau (TP). Based on radiosonde observation (RAOB) and the fifth-generation ECMWF global climate atmospheric reanalysis (ERA5), this study validates the availability and representativeness of FY-4B/GIIRS ATP products in the eastern TP region. Due to the issue of satellite zenith, this study focuses solely on examining the eastern TP region. Under a clear sky, FY-4B/GIIRS ATP exhibits good consistency with RAOB compared to cloudy conditions, with an average root mean square error (RMSE) of 2.57 K. FY-4B/GIIRS tends to underestimate temperatures in the lower layers while overestimating temperatures in the upper layers. The bias varies across seasons. Except for summer, the horizontal and vertical bias distribution patterns are similar, though there are slight differences in values. Despite the presence of bias, FY-4B/GIIRS ATP maintains a good consistency with observations and reanalysis data, indicating commendable product quality. These results demonstrate that it can play a vital role in augmenting the ATP observation network limited by sparse radiosonde stations in the eastern TP, offering crucial data support for numerical weather prediction, weather monitoring, and related meteorological research in this region.

A simple snow temperature index model exposes discrepancies between reanalysis snow water equivalent products

Abstract. Current global reanalyses show marked discrepancies in snow mass and snow cover extent for the Northern Hemisphere. Here, benchmark snow datasets are produced by driving a simple offline snow model, the Brown Temperature Index Model (B-TIM), with temperature and precipitation from three reanalyses. The B-TIM offline snow performs comparably to or better than online (coupled land–atmosphere) reanalysis snow when evaluated against in situ snow measurements. Sources of discrepancy in snow climatologies, which are difficult to isolate when comparing online reanalysis snow products amongst themselves, are partially elucidated by separately bias-adjusting temperature and precipitation in the B-TIM. Interannual variability in snow mass and snow spatial patterns is far more self-consistent amongst offline B-TIM snow products than amongst online reanalysis snow products, and the self-consistent products are more similar to in situ observations, as evaluated in a validation study. Specific artifacts related to temporal inhomogeneity in snow data assimilation are revealed in the analysis. The B-TIM, released here as an open-source, self-contained Python package, provides a simple benchmarking tool for future updates to more sophisticated online and offline snow datasets.

Evaluation of meteorological drought and its impact on crop yield over Afar region, northeast Ethiopia

Drought is a natural hazard caused by a prolonged precipitation deficit that cannot meet human, livestock, and environmental demands. In Ethiopia, frequent and severe droughts increasingly affect the socio-economic and environmental sectors. This study evaluates the current and future projected meteorological drought and its impact on crop yield over the Afar region in northeast Ethiopia. We used surface stations, satellite climate estimates, downscaled atmospheric reanalysis, and regional climate model datasets. We evaluated the occurrence of drought using the Standardized Precipitation Index (SPI) and Standardized Precipitation and Evapotranspiration Index (SPEI) calculated at 3-month and 12-month time scales. The drought vs. regional cereal yield is correlated to explain yield variability in the region. Results showed that more intense droughts were analyzed in 1984, 1985, 2002, 2008, 2009, 2010, 2015, and 2016. Among these years, 1984, 2002, 2008, 2009, and 2015 were the driest years across all locations in the study area. The regression of SPI and SPEI with yield showed that the indices significantly explained (r2 = 0.56 for SPI and 0.18 for SPEI) the observed yield variation. Spatially, more intense drought prevails over the northern, northwestern, and southwestern parts of Afar, where these parts are more prone to severe drought. The projected drought pattern showed increases in the intensity and frequency of drought in the middle and end of the century. The findings of this study are helpful for stakeholders working on drought mitigation in the region. Keywords: Climate dataset, meteorological drought indices, drought projection, pastoral community.

Role of Tropical Disturbances along the South Asian Monsoon Trough in the 2022 Pakistan Floods and the Implications for Interannual Variability

Abstract This study examined the atmospheric circulation patterns that were responsible for the severe flooding that occurred in Pakistan in 2022. It focused on the role of westward-propagating tropical disturbances (TDs) along the South Asian monsoon trough, which are associated with flood-related precipitation events. The study also investigated the interannual precipitation variation in Pakistan over 23 years from 2000 to 2022, using satellite-derived precipitation and atmospheric reanalysis. The results showed high precipitation in southern and central Pakistan, implying tropical influences. The precipitation was the highest in the past 23 years. The massive rainfall was brought by several TDs under the highest water vapor conditions, although a TD of South Asia climatologically tends to weaken before affecting Pakistan. Enhanced easterlies along the Indo-Gangetic Plain, part of the enhanced monsoon trough, supported high water vapor conditions over Pakistan. The high water vapor was also partly supported by the long-lasting active phase of the South Asian monsoon intraseasonal oscillation in 2022. Moreover, the La Niña condition increased moisture transport as a background. On an interannual time scale, the enhanced moisture flux over the Indo-Gangetic Plain resulted in higher precipitation over Pakistan, as occurred in 2022. These high (low) water vapor and precipitation can be partly explained by La Niña (El Niño). However, La Niña and El Niño may not control the TD activity over South Asia. Thus, for 2022, the high water vapor is somewhat empirically predictable, but the active TD activity can be coincidental. Significance Statement In 2022, Pakistan was hit by catastrophic flooding that affected 15% of the population. The analysis of the atmospheric circulation associated with the flooding indicates that weak but rain-bearing tropical disturbances played a significant role in flooding. Several tropical disturbances stayed or moved into Pakistan, bringing massive rainfall although, usually, they tend to weaken before hitting Pakistan. In the past 23 years, La Niña–related weather patterns have partly explained the year-to-year precipitation variation in Pakistan. However, in 2022, the unusually heavy flooding was due to the combined La Niña effect and tropical disturbance activity. Because it is still difficult to predict the seasonal tropical disturbance activity from La Niña, El Niño, and other climatic conditions, seasonal prediction of these floods remains challenging.

High spatiotemporal resolution estimation and analysis of global surface CO concentrations using a deep learning model

Ambient carbon monoxide (CO) is a primary air pollutant that poses significant health risks and contributes to the formation of secondary atmospheric pollutants, such as ozone (O3). This study aims to elucidate global CO pollution in relation to health risks and the influence of natural events like wildfires. Utilizing artificial intelligence (AI) big data techniques, we developed a high-performance Convolutional Neural Network (CNN)-based Residual Network (ResNet) model to estimate daily global CO concentrations at a high spatial resolution of 0.07° from June 2018 to May 2021. Our model integrated the global TROPOMI Total Column of atmospheric CO (TCCO) product and reanalysis datasets, achieving desirable estimation accuracies with R-values (correlation coefficients) of 0.90 and 0.96 for daily and monthly predictions, respectively. The analysis reveals that the CO concentrations were relatively high in northern and central China, as well as northern India, particularly during winter months. Given the significant role of wildfires in increasing surface CO levels, we examined their impact in the Indochina Peninsula, the Amazon Rain Forest, and Central Africa. Our results show increases of 60.0%, 28.7%, and 40.8% in CO concentrations for these regions during wildfire seasons, respectively. Additionally, we estimated short-term mortality cases related to CO exposure in 17 countries for 2019, with China having the highest mortality cases of 23,400 (95% confidence interval: 0–99,500). Our findings highlight the critical need for ongoing monitoring of CO levels and their health implications. The daily surface CO concentration dataset is publicly available and can support future relevant sustainable studies, which is accessible at https://doi.org/10.5281/zenodo.11806178.

Advancing global solar photovoltaic power forecasting with sub-seasonal climate outlooks

Given the high weather dependency of solar photovoltaic energy, accurate weather information ranging from days to weeks in advance are required for stable plant operation and management. This study emphasizes that sub-seasonal climate outlooks can advance global solar power forecasting. We evaluate the capacity factor (CF) for standard test conditions (CFstc), calculated from atmospheric reanalysis of surface solar irradiance and ambient air temperature, by comparing it with actual CF from two solar plants in Korea. Results confirm CFstc as a reliable estimate of actual CF. Application of this methodology to four-week outlooks from two state-of-the-art sub-seasonal climate forecast systems shows a significant anomaly correlation coefficient (ACC) skill for global solar power forecasting at least one week in advance (5–11 forecast days). Regional ACC skills vary at extended lead times, with South Asia, eastern South America and eastern Australia maintaining ACC > 0.6 for up to two weeks (12–18 forecast days). Notably, useful ACC skill persists for up to four weeks (26–32 forecast days) across 70.9 % of global land areas. These findings suggest that sub-seasonal climate outlooks can provide decision-making information in developing more reliable solar energy strategies, highlighting the importance of transdisciplinary collaboration between renewable energy and meteorological sectors.

The West Pacific Teleconnection Drives the Interannual Variability of Autumn Wildfire Weather in the Western United States After 2000

AbstractWildfires pose a significant threat to human society as severe natural disasters. The western United States (US) is one hotspot that has experienced dramatic influences from autumn wildfires especially after 2000, but what has caused its year‐to‐year variations remains poorly understood. By analyzing observational and atmospheric reanalysis datasets, we found that the West Pacific (WP) pattern centered in the western North Pacific acted as a major climatic factor to the post‐2000 autumn wildfire activity by inducing anomalous high pressure over the western US via teleconnections with increased surface temperature, decreased precipitation, and reduced relative humidity. The WP pattern explains about one‐third of the post‐2000 years‐to‐year variance of the western US autumn wildfires. These effects were found to be much weaker in the 1980–1990s, as the active region of WP‐associated high pressure was confined to the eastern North Pacific. Such eastward shift of the WP teleconnection pattern and its resultant, enhanced influence on the weather conditions of western US autumn wildfire after 2000 are also captured by the sea surface temperature (SST)‐forced atmospheric model simulations with the Community Atmosphere Model version 6 (CAM6). The CAM6 ensemble‐mean changes in the WP teleconnection pattern at 2000 is about half of the observed changes, which implies that external radiative forcing and/or SST changes have played an important role in the WP pattern shift. Our results highlight a pressing need to consider the joint impacts of atmospheric internal variability and externally forced climate changes when studying the interannual variations of wildfire activity.

Record-Breaking Marine Heatwave over the Central North Pacific in 2021 Summer: Its Formation Associated with Loss of Central Mode Water

Abstract Marine heatwaves (MHWs) are prolonged extremely high sea surface temperature (SST) events. In the 2021 summer, an intense MHW occurred over the central North Pacific; the SST in September 2021 was the highest in September since 1900, and the warming signal was distributed not only near the sea surface but also below the ocean mixed layer (∼300-m depth). Atmosphere reanalysis data showed westward expansion of the North Pacific subtropical high (NPSH) in the 2021 summer, but both an increase in downward shortwave radiation and a decrease in upward latent heat flux were not so large, and ocean mixed layer heat budget analysis, which also used ocean reanalysis data, revealed that the atmosphere-induced heating is insufficient to form the record-breaking MHW. Argo profiling floats indicated that, in the 2021 summer, the Central Mode Water (CMW)—a huge water mass characterized by vertically uniform properties in depths of 100–500 m—decreased extremely, the thickness of which was less than 20% of the normal. Statistical analysis showed that, from the sea surface to the upper boundary of CMW, the heavier isopycnal surfaces are deeper associated with the decrease in CMW, leading to a weakening of the seasonal pycnocline. Then, this causes the weakening of cooling heat flux associated with the entrainment of subsurface waters into the mixed layer, resulting in surface ocean warming, which in turn contributed to form the MHW in the 2021 summer.

Generation of root zone soil moisture from the integration of an all-weather satellite surface soil moisture estimates and an analytical model: A preliminary result in China

In addition to surface soil moisture (SSM) data, root zone soil moisture (RZSM) is particularly significant for irrigation decision-making and agricultural drought warnings since water uptake generally occurs through the root systems of plants. In this study, a daily/0.05° RZSM dataset covering the major land areas of China was generated from the synergistic use of satellite-derived SSM estimates and a RZSM-SSM analytical model. First, based on the SSM and RZSM datasets generated from the fifth generation of atmospheric reanalysis (ERA5-Land), the Markov Chain Monte Carlo algorithm was applied to optimize the soil moisture analytical relationship (SMAR) model to determine four essential parameters pixel-to-pixel over the study area. In addition, the optimized SMAR parameters were integrated with satellite-derived daily/0.05° SSM to predict the daily/0.05° RZSM in China. Finally, due to the lack of in situ RZSM measurements ground truth at a spatial resolution of 0.05°, two RZSM products, namely the Soil Moisture Active Passive (SMAP)-based RZSM and the China Meteorological Administration Land Data Assimilation System (CLDAS)-based RZSM, were used as references to preliminarily assess the estimated data. The results showed overall fair correlations between SMAP_RZSM and estimated RZSM (correlation coefficient R varying from 0.557 to 0.655), and between CLDAS_RZSM and estimated RZSM (correlation coefficient R varying from 0.496 to 0.596) over four typical days representing spring, summer, autumn, and winter in 2019. Although the proposed approach may overestimate or underestimate RZSM compared to the two referenced products in different seasons, an overall acceptable accuracy with unbiased root mean square error of ∼ 0.070–0.090 cm3/cm3 was obtained.

The Impact of Climate Variability on Cattle Heat Stress in Vanuatu

Heat stress is a climate extreme that impacts cattle health, fertility, feed intake, production, and well-being. In Vanuatu, the beef industry is crucial to local livelihoods and the nation’s economy, thus the objective of this study was to examine the impact of heat stress on cattle health and production. This study uses the Heat Load Index (HLI) and Accumulated Heat Load (AHL) as proxies to assess the impact of heat stress on cattle in Vanuatu over a 30-year period (1994–2023), using the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5) data. The analysis examines historical patterns of heat stress in cattle across Vanuatu, identifying more instances of heat stress occurring during the wet season due to characteristically elevated temperatures, humidity, and low wind speeds. Findings also suggest that El Niño events may increase the intensity and duration of heat stress events. These insights inform the development of an Early Warning System for heat stress in cattle, establishing a crucial foundation for targeted adaptation strategies aimed at enhancing the resilience and sustainability of Vanuatu’s beef industry to climate variability and change.

Revisiting the inconsistent influence of El Niño-Southern Oscillation on the equatorial Atlantic

Abstract The impact of El Niño-Southern Oscillation (ENSO) on the equatorial Atlantic Zonal Mode (AZM) is investigated using two atmospheric reanalysis products and 44 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). While the impact of ENSO on equatorial Atlantic SST is inconsistent, as previously reported, there is a very robust influence of decaying ENSO events in boreal spring on the contemporaneous surface zonal winds over the equatorial Atlantic, with El Niño events associated with anomalous easterlies that cool the equatorial Atlantic. This dynamic impact is counteracted by a thermodynamic impact, in which El Niño events cause changes in surface heat fluxes that lead to warming over the tropical Atlantic. The thermodynamic influence is active throughout the lifecycle of the ENSO event, but the dynamic influence is highly seasonal with a pronounced peak in boreal spring. Thus, a quickly decaying El Niño event will lead to warming, while a slowly decaying event will lead to cooling in the equatorial Atlantic. The ENSO-AZM relation is further complicated by partially independent Atlantic variability, in particular that associated with the South Atlantic subtropical dipole (SASD). Prediction experiments with a simple linear regression model support the role of the SASD as an AZM precursor. Many CMIP6 models show skillful predictions of the June-July-August AZM based on SST indices in the preceding December-January-February, with correlations above 0.5. When predictions are restricted to ENSO years, even more models exceed this threshold, but skill in the reanalyses remains relatively low, possibly due to insufficient training data.

Accurate and efficient AI-assisted paradigm for adding granularity to ERA5 precipitation reanalysis

Scientific inquiry has long relied on deterministic algorithms for systematic problem-solving and predictability. However, the rise of artificial intelligence (AI) has revolutionized data analysis, allowing us to uncover complex patterns in large datasets. In this study, we combine these two approaches by using AI to improve the reconstruction of past precipitation events, which is crucial for understanding climate change. Our objective is to leverage AI to map large-scale atmospheric proxies from the ERA5 climate reanalysis and multi-satellite historical precipitation data from the NASA-IMERG GPM constellation to observed precipitation, enhancing the accuracy and the resolution of climate reanalysis. Accurate climate reanalyses are essential, as they provide the most realistic representations of past atmospheric conditions, serving as benchmarks against which climate models are validated. Our AI-enhanced method offers a more accurate and computationally efficient solution compared to deterministic high-resolution precipitation downscaling methods. Additionally, it shows the capability to generalize predictions to new, previously unobserved locations, making it applicable across various regions. By integrating AI with traditional reanalysis techniques, we open up new opportunities for climate science and geosciences, with the potential to improve the accuracy and reliability of climate data, contributing to a better understanding of climate dynamics.

Recommended coupling to global meteorological fields for long-term tracer simulations with WRF-GHG

Abstract. Atmospheric transport models are often used to simulate the distribution of greenhouse gases (GHGs). This can be in the context of forward modeling of tracer transport using surface–atmosphere fluxes or flux estimation through inverse modeling, whereby atmospheric tracer measurements are used in combination with simulated transport. In both of these contexts, transport errors can bias the results and should therefore be minimized. Here, we analyze transport uncertainties in the commonly used Weather Research and Forecasting (WRF) model coupled with the greenhouse gas module (WRF-GHG), enabling passive tracer transport simulation of CO2 and CH4. As a mesoscale numerical weather prediction model, WRF's transport is constrained by global meteorological fields via initialization and at the lateral boundaries of the domain of interest. These global fields were generated by assimilating various meteorological data to increase the accuracy of modeled fields. However, in limited-domain models like WRF, the winds in the center of the domain can deviate considerably from these driving fields. As the accuracy of the wind speed and direction is critical to the prediction of tracer transport, maintaining a close link to the observations across the simulation domain is desired. On the other hand, a link that is too close to the global meteorological fields can degrade performance at smaller spatial scales that are better represented by the mesoscale model. In this work, we evaluated the performance of strategies for keeping WRF's meteorology compatible with meteorological observations. To avoid the complexity of assimilating meteorological observations directly, two main strategies of coupling WRF-GHG with ERA5 meteorological reanalysis data were tested over a 2-month-long simulation over the European domain: (a) restarting the model daily with fresh initial conditions (ICs) from ERA5 and (b) nudging the atmospheric winds, temperatures, and moisture to those of ERA5 continuously throughout the simulation period, using WRF's built-in four-dimensional data assimilation (FDDA) in grid-nudging mode. Meteorological variables and simulated mole fractions of CO2 and CH4 were compared against observations to assess the performance of the different strategies. We also compared planetary boundary layer height (PBLH) with radiosonde-derived estimates. Either nudging or daily restarts similarly improved the meteorology and GHG transport in our simulations, with a small advantage of using both methods in combination. However, notable differences in soil moisture were found that accumulated over the course of the simulation when not using frequent restarts. The soil moisture drift had an impact on the simulated PBLH, presumably via changing the Bowen ratio. This is partially mitigated through nudging without requiring daily restarts, although not entirely alleviated. Soil moisture drift did not have a noticeable impact on GHG performance in our case, likely because it was dominated by other errors. However, since the PBLH is critical for accurately simulating GHG transport, we recommend transport model setups that tie soil moisture to observations. Our method of frequently re-initializing simulations with meteorological reanalysis fields proved suitable for this purpose.

Integrating observational and modelled data to advance the understanding of heat stress effects on pregnant subsistence farmers in the gambia

Studies on the effect of heat stress on pregnant women are scarce, particularly in highly vulnerable populations. To support the risk assessment of pregnant subsistence farmers in the West Kiang district, The Gambia we conducted a study on the pathophysiological effects of extreme heat stress and assessed the applicability of heat stress indices. From ERA5 climate reanalysis we added location-specific modelled solar radiation to datasets of a previous observational cohort study involving on-site measurements of 92 women working in the heat. Associations between physiological and environmental variables were assessed through Pearson correlation coefficient analysis, mixed effect linear models with random intercepts per participant and confirmatory composite analysis. We found Pearson correlations between r-values of 0 and 0.54, as well as independent effects of environmental variables on skin- and tympanic temperature, but not on heart rate, within a confidence interval of 98%. Pregnant women experienced stronger pathophysiological effects from heat stress in their third rather than in their second trimester. Environmental heat stress significantly altered maternal heat strain, particularly under humid conditions above a 50% relative humidity threshold, demonstrating interactive effects. Based on our results, we recommend including heat stress indices (e.g. UTCI or WBGT) in local heat-health warning systems.

Toward an advanced physics-based scheme for retrieving land surface emissivity and temperature based on Fengyun-3D MERSI-II daytime mid-infrared data.

The hybrid nature of the mid-infrared (MIR) spectrum complicates the separation of reflected solar irradiance from total energy. Consequently, existing studies rarely use MIR satellite data alone for retrieving land surface temperature (LST) and land surface emissivity (LSE). In this study, we developed What we believe to be a novel physics-based approach to retrieve LSE and LST using MIR channel data from the MEdium Resolution Spectral Imager II (MERSI-II) onboard China's new-generation polar-orbiting meteorological satellite Fengyun-3D (FY-3D). MERSI-II includes two MIR channels (channels 20 and 21) with a spatial resolution of 1 km, suitable for applying the split-window (SW) algorithm. First, considering the unequal but linearly related land surface bidirectional reflectivity (LSR) in channels 20 and 21, we propose an improved nonlinear SW algorithm. This algorithm, combined with the radiative transfer equation (RTE), accurately retrieves LSR from MIR data. Second, using a kernel-driven bidirectional reflectance distribution function (BRDF) model, the RossThick-LiSparse-R model, we estimate hemispherical directional reflectance from the time series of LSRs (10 days) and subsequently retrieve LSE based on Kirchhoff's law. Atmospheric correction is performed using ERA-5 atmospheric reanalysis data with the radiative transfer (RT) code (MODTRAN 5.2). Finally, LST is retrieved using the RTE in the MIR spectral region. The retrieved LSR was compared with those fitted using the BRDF model, yielding a root mean square error (RMSE) < 0.006 and a bias < 0.003. Cross-validation using the MODIS LSE and LST products (MYD11C1) as a reference showed that the RMSE of the retrieved LSE over 10 days was < 0.027 with a bias < 0.023. For the retrieved LST, the RMSE was < 1.8 K with a bias < 0.7 K. Overall, the proposed method demonstrates potential for retrieving global LSE and LST from MERSI-II MIR data, contributing to advancements in related applications.

A Copernicus-based evapotranspiration dataset at 100 m spatial resolution over four Mediterranean basins

Abstract. Evapotranspiration (ET) is responsible for regulating the hydrological cycle, with a relevant impact on air humidity and precipitation that is particularly important in the context of acute drought events in recent years. With the intensification of rainfall deficits and extreme heat events, the Mediterranean region requires regular monitoring to enhance water resource management. Even though remote sensing provides spatially continuous information for estimating ET on large scales, existing global products with spatial resolutions ≥ 0.5 km are insufficient for capturing spatial detail at a local level. In the framework of ESA's 4DMED-Hydrology project, we generate an ET dataset at both high spatial and high temporal resolutions using the Priestley–Taylor Two-Source Energy Balance (TSEB-PT) model driven by Copernicus satellite data. We build an automatic workflow to generate a 100 m ET product by combining data from Sentinel-2 (S2) MultiSpectral Instrument (MSI) and Sentinel-3 (S3) land surface temperature (LST) with ERA5 climate reanalysis derived within the period 2017–2021 over four Mediterranean basins in Italy, Spain, France, and Tunisia (Po, Ebro, Hérault, and Medjerda). First, original S2 data are pre-processed before deriving 100 m inputs for the ET estimation. Next, biophysical variables, like leaf area index and fractional vegetation cover, are generated, and then they are temporally composited within a 10 d window according to S3 acquisitions. Consequently, decadal S2 mosaics are used to derive the remaining TSEB-PT inputs. In parallel, we sharpen 1 km S3 by exploiting the dependency between coarse-resolution LST and 100 m S2 reflectances using a decision tree algorithm. Afterwards, climate forcings are utilized to model energy fluxes and then for daily ET retrieval. The daily ET composites demonstrate reasonable TSEB-PT estimates. Based on the validation results against eight eddy covariance (EC) towers between 2017 and 2021, the model predicts 100 m ET with an average RMSE of 1.38 mm d−1 and a Pearson coefficient equal to 0.60. Regardless of some constraints mostly related to the high complexity of EC sites, TSEB-PT can effectively estimate 100 m ET, which opens up new opportunities for monitoring the hydrological cycle on a regional scale. The full dataset is freely available at https://doi.org/10.48784/b90a02d6-5d13-4acd-b11c-99a0d381ca9a, https://doi.org/10.48784/fb631817-189f-4b57-af6a-38cef217bad3, https://doi.org/10.48784/70cd192c-0d46-4811-ad1d-51a09734a2e9, and https://doi.org/10.48784/7abdbd94-ddfe-48df-ab09-341ad2f52e47 for the Ebro, Hérault, Medjerda, and Po catchments, respectively (Bartkowiak et al., 2023a–d).

Changes in the Climate Comfort of the Coast of Spain (1940-2022)

The Spanish coastal regions register specific climatic conditions due to the combination of mild temperatures with little variation throughout the year, high relative humidity and the influence of maritime storms. In summer, the climatic comfort conditions are excessively hot, especially on the Mediterranean coast of Spain. Understanding these conditions and analysing the temporal evolution of recent decades, as well as regional differences, is fundamental for future summer tourism planning in the coming decades. This study analyses the principal 37 coastal tourist hubs of Spain grouped into 10 large regions (the Atlantic, Cantabrian, Mediterranean coasts, and the two archipelagos of the Balearic and Canary Islands). Daily data drawn from the ERA-5 (Copernicus) atmospheric reanalysis from 1940 to 2022 have been used (mean air temperature, mean relative humidity, and mean wind speed), with which the Climate Comfort Index has been calculated (CCI) by González (1998). The results show a significant reduction of the CCI in all the coastal areas analyzed, being more relevant in winter (-0.10 decade). The decrease in the index implies a decrease in cold thresholds and an expansion of comfort throughout the study area, especially in the central Mediterranean and Cantabrian Sea. For its part, in summer, the most important decreases (-0.07 and -0.08 / decade) show an increase in the most important climatic discomfort on the Cantabrian Coast – Euskal Kostaldea and on the Costa Brava-El Garraf, with a significant intensification and expansion temporary thermal sensation of heat. In other coastal sectors, in recent years, the climatic thresholds of heat and extreme heat have been reached for the first time.

Multi-objective calibration and evaluation of the ORCHIDEE land surface model over France at high resolution

Abstract. Here we present a strategy to obtain a reliable hydrological simulation over France with the ORCHIDEE land surface model. The model is forced by the SAFRAN atmospheric reanalysis at 8 km resolution and hourly time steps from 1959 to 2020 and by a high-resolution DEM (around 1.3 km in France). Each SAFRAN grid cell is decomposed into a graph of hydrological transfer units (HTUs) based on the higher-resolution DEM to better describe lateral water movements. In particular, it is possible to accurately locate 3507 stations among the 4081 stations collected from the national hydrometric network HydroPortail (filtered to drain an upstream area larger than 64 km2). A simple trial-and-error calibration is conducted by modifying selected parameters of ORCHIDEE to reduce the biases of the simulated water budget compared to the evapotranspiration products (the GLEAM and FLUXCOM datasets) and the HydroPortail observations of river discharge. The simulation that is eventually preferred is extensively assessed with classic goodness-of-fit indicators complemented by trend analysis at 1785 stations (filtered to have records for at least 8 entire years) across France. For example, the median bias of evapotranspiration is −0.5 % against GLEAM (−4.3 % against FLUXCOM), the median bias of river discharge is 6.3 %, and the median Kling–Gupta efficiency (KGE) of square-rooted river discharge is 0.59. These indicators, however, exhibit a large spatial variability, with poor performance in the Alps and the Seine sedimentary basin. The spatial contrasts and temporal trends of river discharge across France are well represented with an accuracy of 76.4 % for the trend sign and an accuracy of 62.7 % for the trend significance. Although it does not yet integrate human impacts on river basins, the selected parameterization of ORCHIDEE offers a reliable historical overview of water resources and a robust configuration for climate change impact analysis at the nationwide scale of France.