scholarly journals Evaluation of HOAPS-3 Ocean Surface Freshwater Flux Components

2011 ◽  
Vol 50 (2) ◽  
pp. 379-398 ◽  
Author(s):  
Axel Andersson ◽  
Christian Klepp ◽  
Karsten Fennig ◽  
Stephan Bakan ◽  
Hartmut Grassl ◽  
...  
Keyword(s):  
Satellite Data ◽  
Storm Track ◽  
Ocean Surface ◽  
Satellite Observation ◽  
Reanalysis Data ◽  
Global Ocean ◽  
Freshwater Flux ◽  
Near Surface ◽  
Sampling Statistics

Abstract Today, latent heat flux and precipitation over the global ocean surface can be determined from microwave satellite data as a basis for estimating the related fields of the ocean surface freshwater flux. The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) is the only generally available satellite-based dataset with consistently derived global fields of both evaporation and precipitation and hence of freshwater flux for the period 1987–2005. This paper presents a comparison of the evaporation E, precipitation P, and the resulting freshwater flux E − P in HOAPS with recently available reference datasets from reanalysis and other satellite observation projects as well as in situ ship measurements. In addition, the humidity and wind speed input parameters for the evaporation are examined to identify sources for differences between the datasets. Results show that the general climatological patterns are reproduced by all datasets. Global mean time series often agree within about 10% of the individual products, while locally larger deviations may be found for all parameters. HOAPS often agrees better with the other satellite-derived datasets than with the in situ or the reanalysis data. The agreement usually improves in regions of good in situ sampling statistics. The biggest deviations of the evaporation parameter result from differences in the near-surface humidity estimates. The precipitation datasets exhibit large differences in highly variable regimes with the largest absolute differences in the ITCZ and the largest relative biases in the extratropical storm-track regions. The resulting freshwater flux estimates exhibit distinct differences in terms of global averages as well as regional biases. In comparison with long-term mean global river runoff data, the ocean surface freshwater balance is not closed by any of the compared fields. The datasets exhibit a positive bias in E − P of 0.2–0.5 mm day−1, which is on the order of 10% of the evaporation and precipitation estimates.

The limitations of in situ data for validating satellite-derived spatio-temporal ocean products.

2021 ◽  
Author(s):  
Rory Scarrott ◽  
Fiona Cawkwell ◽  
Mark Jessopp ◽  
Caroline Cusack
Keyword(s):  
Surface Temperature ◽  
Surface Heterogeneity ◽  
Ocean Surface ◽  
Bias Reduction ◽  
Data Validation ◽  
Near Surface ◽  
In Situ Data ◽  
Spatio Temporal ◽  
Image Pixels

<p>The Ocean-surface Heterogeneity MApping (OHMA) algorithm is an objective, replicable approach to map the spatio-temporal heterogeneity of ocean surface waters. It is used to processes hypertemporal, satellite-derived data and produces a single-image surface heterogeneity (SH) dataset for the selected parameter of interest. The product separates regions of dissimilar temporal characteristics. Data validation is challenging because it requires In-situ observations at spatial and temporal resolutions comparable to the hyper-temporal inputs. Validating this spatio-temporal product highlighted the need to optimise existing vessel-based data collection efforts, to maximise exploitation of the rapidly-growing hyper-temporal data resource.</p><p>For this study, the SH was created using hyper-temporal 1km resolution satellite derived Sea Surface Temperature (SST) data acquired in 2011. Underway ship observations of near surface temperature collected on multiple scientific surveys off the Irish coast in 2011 were used to validate the results. The most suitable underway ship SST data were identified in ocean areas sampled multiple times and with representative measurements across all seasons.</p><p>A 3-stage bias reduction approach was applied to identify suitable ocean areas. The first bias reduction addressed temporal bias, i.e., the temporal spread of available In-situ ship transect data across each satellite image pixel. Only pixels for which In-situ data were obtained at least once in each season were selected; resulting in 14 SH image pixels deemed suitable out of a total of 3,677 SH image pixels with available In-situ data. The second bias reduction addressed spatial bias, to reduce the influence of over-sampled areas in an image pixel with a sub-pixel approach. Statistical dispersion measures and statistical shape measures were calculated for each of the sets of sub-pixel values. This gave heterogeneity estimates for each cruise transit of a pixel area. The third bias reduction addressed bias of temporally oversampled seasons. For each transit-derived heterogeneity measure, the values within each season were averaged, before the annual average value was derived across all four seasons in 2011.</p><p>Significant associations were identified between satellite SST-derived SH values, and In-situ heterogeneity measures related to shape; absolute skewness (Spearman’s Rank, n=14, ρ[12]= +0.5755, P<0.05), and kurtosis (Spearman’s Rank, n=14, ρ[12] = 0.5446, P < 0.05). These are a consequence of (i) locally-extreme measurements, and/or (ii) increased presence of sharp transitions detected spatially by In-situ data. However, the number and location of suitable In-situ validation sites precluded a robust validation of the SH dataset (14 pixels located in Irish waters, for a dataset spanning the North Atlantic). This requires more targeted data. The approach would have benefited from more samples over the winter season, which would have enabled more offshore validation sites to be incorporated into the analysis. This is a challenge that faces satellite product developers, who want to deliver spatio-temporal information to new markets. There is a significant opportunity for dedicated, transit-measured (e.g. Ferry box data), validation sites to be established. These could potentially synergise with key nodes in global shipping routes to maximise data collected by vessels of opportunity, and ensure consistent data are collected over the same area at regular intervals.</p>

Ocean Salinity changes in the global ocean under global warming conditions Part 1: Mechanisms in a strong warming scenario

2021 ◽  
pp. 1-56
Author(s):  
Anju Sathyanarayanan ◽  
Armin Köhl ◽  
Detlef Stammer
Keyword(s):  
Large Scale ◽  
Water Cycle ◽  
Spatial Scales ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Dynamical Response ◽  
Freshwater Flux ◽  
Near Surface ◽  
Salinity Changes ◽  
Circulation Changes

AbstractWe investigate mechanisms underlying salinity changes projected to occur under strong representative concentration pathway (RCP) 8.5 forcing conditions. The study is based on output of the Max Planck Institute Earth System Model Mixed Resolution (MPI-ESM-MR) run with an ocean resolution of 0.4°. In comparison to the present-day oceanic conditions, sea surface salinity (SSS) increases towards the end of the 21st century in the tropical and the subtropical Atlantic. In contrast, a basin-wide surface freshening can be observed in the Pacific and Indian Oceans. The RCP8.5 scenario of the MPI-ESM-MR with a global surface warming of ~2.3°C marks a water cycle amplification of 19 %, which is equivalent to ~8%°C−1 and thus close to the water cycle amplification predicted according to the Clausius–Clapeyron (CC) relationship (~7%°C−1). Large scale global SSS changes are driven by adjustments of surface freshwater fluxes. On smaller spatial scales, it is predominantly advection related to circulation changes that affects near-surface SSS. With respect to subsurface salinity, it is changes in surface freshwater flux that drive their changes over the upper 500 m of the subtropical Pacific and Indian oceans by forcing changes in water mass formation (spice signal). In the subtropical Atlantic Ocean, in contrast, the dynamical response associated with wind stress, circulation changes and associated heaving of isopycnals is equally important in driving subsurface salinity changes over the upper 1000 m.

scholarly journals High-Resolution Sea Surface Temperature and Salinity in the Global Ocean from Raw Satellite Data

2019 ◽  
Vol 11 (19) ◽  
pp. 2191 ◽  
Author(s):  
Encarni Medina-Lopez ◽  
Leonardo Ureña-Fuentes
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Satellite Data ◽  
Google Earth ◽  
Sea Surface ◽  
Global Ocean ◽  
The Neural Network ◽  
Wide Range ◽  
Sentinel 2

The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m × 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82 % and 84 % for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 ∘ C and 0 . 4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.

scholarly journals Integration of HF Radar Observations for an Enhanced Coastal Mean Dynamic Topography

2020 ◽  
Vol 7 ◽  
Author(s):  
Ainhoa Caballero ◽  
Sandrine Mulet ◽  
Nadia Ayoub ◽  
Ivan Manso-Narvarte ◽  
Xabier Davila ◽  
...  
Keyword(s):  
High Resolution ◽  
Sea Level ◽  
Satellite Data ◽  
Coastal Areas ◽  
Hf Radar ◽  
Global Ocean ◽  
Dynamic Topography ◽  
Mean Dynamic Topography ◽  
Radar Measurements

Satellite altimeters provide continuous information of the sea level variability and mesoscale processes for the global ocean. For estimating the sea level above the geoid and monitoring the full ocean dynamics from altimeters measurements, a key reference surface is needed: The Mean Dynamic Topography (MDT). However, in coastal areas, where, in situ measurements are sparse and the typical scales of the motion are generally smaller than in the deep ocean, the global MDT solutions are less accurate than in the open ocean, even if significant improvement has been done in the past years. An opportunity to fill in this gap has arisen with the growing availability of long time-series of high-resolution HF radar surface velocity measurements in some areas, such as the south-eastern Bay of Biscay. The prerequisite for the computation of a coastal MDT, using the newly available data of surface velocities, was to obtain a robust methodology to remove the ageostrophic signal from the HF radar measurements, in coherence with the scales resolved by the altimetry. To that end, we first filtered out the tidal and inertial motions, and then, we developed and tested a method that removed the Ekman component and the remaining divergent part of the flow. A regional high-resolution hindcast simulation was used to assess the method. Then, the processed HF radar geostrophic velocities were used in synergy with additional in situ data, altimetry, and gravimetry to compute a new coastal MDT, which shows significant improvement compared with the global MDT. This study showcases the benefit of combining satellite data with continuous, high-frequency, and synoptic in situ velocity data from coastal radar measurements; taking advantage of the different scales resolved by each of the measuring systems. The integrated analysis of in situ observations, satellite data, and numerical simulations has provided a further step in the understanding of the local ocean processes, and the new MDT a basis for more reliable monitoring of the study area. Recommendations for the replicability of the methodology in other coastal areas are also provided. Finally, the methods developed in this study and the more accurate regional MDT could benefit present and future high-resolution altimetric missions.

scholarly journals Mean Dynamic Topography of the Ocean Derived from Satellite and Drifting Buoy Data Using Three Different Techniques*

2009 ◽  
Vol 26 (9) ◽  
pp. 1910-1919 ◽  
Author(s):  
Nikolai Maximenko ◽  
Peter Niiler ◽  
Luca Centurioni ◽  
Marie-Helene Rio ◽  
Oleg Melnichenko ◽  
...  
Keyword(s):  
Ocean Surface ◽  
Horizontal Resolution ◽  
Global Ocean ◽  
Momentum Balance ◽  
Small Scale ◽  
Western Boundary ◽  
Dynamic Topography ◽  
Geoid Model ◽  
Near Surface ◽  
Mean Dynamic Topography

Abstract Presented here are three mean dynamic topography maps derived with different methodologies. The first method combines sea level observed by the high-accuracy satellite radar altimetry with the geoid model of the Gravity Recovery and Climate Experiment (GRACE), which has recently measured the earth’s gravity with unprecedented spatial resolution and accuracy. The second one synthesizes near-surface velocities from a network of ocean drifters, hydrographic profiles, and ocean winds sorted according to the horizontal scales. In the third method, these global datasets are used in the context of the ocean surface momentum balance. The second and third methods are used to improve accuracy of the dynamic topography on fine space scales poorly resolved in the first method. When they are used to compute a multiyear time-mean global ocean surface circulation on a 0.5° horizontal resolution, both contain very similar, new small-scale midocean current patterns. In particular, extensions of western boundary currents appear narrow and strong despite temporal variability and exhibit persistent meanders and multiple branching. Also, the locations of the velocity concentrations in the Antarctic Circumpolar Current become well defined. Ageostrophic velocities reveal convergent zones in each subtropical basin. These maps present a new context in which to view the continued ocean monitoring with in situ instruments and satellites.

scholarly journals Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

2021 ◽  
Vol 25 (1) ◽  
pp. 17-40
Author(s):  
Hylke E. Beck ◽  
Ming Pan ◽  
Diego G. Miralles ◽  
Rolf H. Reichle ◽  
Wouter A. Dorigo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Satellite Data ◽  
Irrigation Scheduling ◽  
Open Loop ◽  
Spatiotemporal Variability ◽  
Near Surface ◽  
Satellite Retrievals ◽  
Performance Ranking

Abstract. Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as “open-loop” models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3ESWI, SMOSSWI, AMSR2SWI, and ASCATSWI, with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.

scholarly journals Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

2020 ◽  
Author(s):  
Hylke E. Beck ◽  
Ming Pan ◽  
Diego G. Miralles ◽  
Rolf H. Reichle ◽  
Wouter A. Dorigo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Satellite Data ◽  
Irrigation Scheduling ◽  
Open Loop ◽  
Spatiotemporal Variability ◽  
Near Surface ◽  
Satellite Retrievals ◽  
Performance Ranking

Abstract. Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as open-loop models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo and six estimates from the HBV model with three precipitation inputs (ERA5, IMERG, and MSWEP) and with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5-cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. The median R ± interquartile range across all sites and products in each category was 0.66 ± 0.30 for the satellite products, 0.69 ± 0.25 for the open-loop models, and 0.72 ± 0.22 for the models with satellite data assimilation. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3E, SMOS, AMSR2, and ASCAT, with the L-band-based SMAPL3E (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCI), MeMo performed better on average (median R of 0.72 versus 0.67), mainly due to the inclusion of SMAPL3E. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.

Global Air–Sea Fluxes of Heat, Fresh Water, and Momentum: Energy Budget Closure and Unanswered Questions

2019 ◽  
Vol 11 (1) ◽  
pp. 227-248 ◽  
Author(s):  
Lisan Yu
Keyword(s):  
Surface Energy ◽  
Fresh Water ◽  
Prediction Models ◽  
Weather Prediction ◽  
Ocean Surface ◽  
Surface Radiation ◽  
Global Ocean ◽  
Personal Perspective ◽  
Near Surface ◽  
Water Budgets

The ocean interacts with the atmosphere via interfacial exchanges of momentum, heat (via radiation and convection), and fresh water (via evaporation and precipitation). These fluxes, or exchanges, constitute the ocean-surface energy and water budgets and define the ocean's role in Earth's climate and its variability on both short and long timescales. However, direct flux measurements are available only at limited locations. Air–sea fluxes are commonly estimated from bulk flux parameterization using flux-related near-surface meteorological variables (winds, sea and air temperatures, and humidity) that are available from buoys, ships, satellite remote sensing, numerical weather prediction models, and/or a combination of any of these sources. Uncertainties in parameterization-based flux estimates are large, and when they are integrated over the ocean basins, they cause a large imbalance in the global-ocean budgets. Despite the significant progress that has been made in quantifying surface fluxes in the past 30 years, achieving a global closure of ocean-surface energy and water budgets remains a challenge for flux products constructed from all data sources. This review provides a personal perspective on three questions: First, to what extent can time-series measurements from air–sea buoys be used as benchmarks for accuracy and reliability in the context of the budget closures? Second, what is the dominant source of uncertainties for surface flux products, the flux-related variables or the bulk flux algorithms? And third, given the coupling between the energy and water cycles, precipitation and surface radiation can act as twin budget constraints—are the community-standard precipitation and surface radiation products pairwise compatible?

Overview of the CCI+SSS project

2020 ◽  
Author(s):  
Jacqueline Boutin ◽  
Nicolas Reul ◽  
Julia Koehler ◽  
Adrien Martin ◽  
Rafael Catany ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Climate Variability ◽  
Satellite Data ◽  
Water Cycle ◽  
Global Ocean ◽  
Variable Regions ◽  
New Findings ◽  
Band Radiometer

<p>Sea Surface Salinity (SSS) is an Essential Climate Variable (ECV) that plays a fundamental role in the density-driven global ocean circulation, the water cycle, and climate. The satellite SSS observation from the Soil Moisture and Ocean Salinity (SMOS), Aquarius, and Soil Moisture Active Passive (SMAP) missions have provided an unprecedented opportunity to map SSS over the global ocean since 2010 at 40-150km scale with a revisit every 2 to 3 days. This observation capability has no historic precedent and has brought new findings concerning the monitoring of SSS variations related with climate variability such as El Niño-Southern Oscillation, Indian Ocean Dipole, and Madden-Julian Oscillation, and the linkages of the ocean with different elements of the water cycle such as evaporation and precipitation and continental runoff. It has enhanced the understanding of various ocean processes such as tropical instability waves, Rossby waves, mesoscale eddies and related salt transport, salinity fronts, hurricane haline wake, river plume variability, cross-shelf exchanges. There are also emerging use of satellite SSS to study ocean biogeochemistry, e.g. linked to air-sea CO<sub>2</sub> fluxes.</p><p>Following the success of the initial oceanographic studies implementing this new variable, the European Space Agency (ESA) Climate Change Initiative CCI+SSS project (2018-2020) aims at generating improved calibrated global SSS fields over 10 years period (2010-2019) from all available satellite L-band radiometer measurements, extended at regional scale to 2002-2019 from C-band radiometer measurements. It fully exploits the ESA/Earth explorer SMOS mission complemented with SMAP and AQUARIUS satellite missions. The project gathers teams involved in earth observation remote sensing, in the validation of satellite data and in climate variability study. In this presentation, we will present the first CCI+SSS product released to the scientific community (https://catalogue.ceda.ac.uk/uuid/9ef0ebf847564c2eabe62cac4899ec41). The comparisons with in situ ground truth indicate much better performances than the ones obtained with a single satellite data product, with global rmsd against in situ references of 0.16 pss. Large scale interannual variability is successfully reproduced and SSS variability in very variable regions like the Bay of Bengale and in river plumes in the Atlantic Ocean is very satisfactory, confirming the usefulness of these products for scientific studies. Nevertheless we also identify some caveats that will be discussed as well as the ways envisaged to resolve part of them in the next version of the product to be delivered publicly in Summer 2020.</p><p>The ESA CCI+SSS consortium gathers scientists and engineers from various European research institutes and companies (LOCEAN/IPSL, LOPS, University of Hamburg, NOC, ICM, ARGANS, ACRI-st, ODL) and is conducted in collaboration with US colleagues from NASA and Remote Sensing System.</p>

Temperature monitoring in mountain regions using reanalyses: Lessons from the Alps

2020 ◽  
Author(s):  
Simon C. Scherrer ◽  
Sven Kotlarski
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Reanalysis Data ◽  
Swiss Alps ◽  
Mountain Regions ◽  
Near Surface ◽  
The Alps ◽  
Regional Reanalysis ◽  
High Elevations

<p>The monitoring of near-surface temperature is a fundamental task of climatology that remains especially challenging in mountain regions. Here we assess the regional monitoring capabilities of modern reanalysis products in the well-monitored northern Swiss Alps during the last 20 to almost 60 years. Monthly and seasonal 2 m air temperature (T2m) anomalies of the global ERA5 and the three regional reanalysis products HARMONIE, MESCAN-SURFEX and COSMO-REA6 are evaluated against high quality in situ observational data for a low elevation (foothills) mean, and a high elevation (Alpine) mean. All reanalysis products show a good year-round performance for the foothills with the global reanalysis ERA5 showing the best overall performance. The high-resolution regional reanalysis COSMO-REA6 clearly performs best for the Alpine mean, especially in winter. Most reanalysis data sets show deficiencies at high elevations in winter and considerably overestimate recent T2m trends in winter. This stresses the fact that even in the most recent decades utmost care is required when using reanalysis data for near-surface temperature trend assessments in mountain regions. Our results indicate that a high-resolution model topography is an important prerequisite for an adequate monitoring of winter T2m using reanalysis data at high elevations in the Alps. Assimilating T2m remains challenging in highly complex terrain. The remaining shortcomings of modern reanalyses also highlight the continued need for a reliable and dense in situ observational monitoring network in mountain regions.</p><p> </p>

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