A global quantitative characterization of offshore wind power density is presented over the economic exclusive zones (EEZs), at annual and seasonal scales, based on the recently released ECMWF ERA-5 reanalysis. Compared to its predecessors, ERA-5 features increased spatial and temporal resolutions, along with improved model parameterizations and data assimilation methodologies. Consequently, the present study represents an updated benchmark of wind resources over the global oceans. Further robustness to this argument is provided by our results showing that of the EEZs with large wind energy potential are characterized by regional-scale circulation features (coastal jets and breezes, land-sea and topographic circulations), known require high model resolution to be accurately simulated. Additionally, it is demonstrated that larger errors (up to 50%) can result from estimating wind power density from logarithmically extrapolated near-surface wind speed when compared to using high-resolution model level information. Coarser (daily) temporal resolution is also found to cause widespread increases in offshore wind power density errors between 10% and 30% compared to higher (hourly) resolution data. Further leveraging on the high vertical resolution of ERA-5, we show the large benefit of future increases in hub-heights from 100 m to 250 m for global offshore wind resources. Such technological advance results in widespread gains (ranging between +5% and +50%) throughout the global EEZs and no losses for all seasons. Moreover, the gains are >+10% for all regions with large wind energy potential at 100 m and >+40% over the Arctic Pacific sector and in regions characterized by the presence of coastal low-level jets.

Land surface temperature (LST) is a key variable in surface-atmosphere energy and water exchanges. The main goals of this study are to (i) evaluate the LST of the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim and ERA5 reanalyses over Iberian Peninsula using the Satellite Application Facility on Land Surface Analysis (LSA-SAF) product and to (ii) understand the main drivers of the LST errors in the reanalysis. Simulations with the ECMWF land-surface model in offline mode (uncoupled) were carried out over the Iberian Peninsula and compared with the reanalysis data. Several sensitivity simulations were performed in a confined domain centered in Southern Portugal to investigate potential sources of the LST errors. The Copernicus Global Land Service (CGLS) fraction of green vegetation cover (FCover) and the European Space Agency’s Climate Change Initiative (ESA-CCI) Land Cover dataset were explored. We found a general underestimation of daytime LST and slightly overestimation at night-time. The results indicate that there is still room for improvement in the simulation of LST in ECMWF products. Still, ERA5 presents an overall higher quality product in relation to ERA-Interim. Our analysis suggested a relation between the large daytime cold bias and vegetation cover differences between (ERA5 and CGLS FCocver) with a correlation of −0.45. The replacement of the low and high vegetation cover by those of ESA-CCI provided an overall reduction of the large Tmax biases during summer. The increased vertical resolution of the soil at the surface, has a positive impact, but much smaller when compared with the vegetation changes. The sensitivity of the vegetation density parameter, that currently depends on the vegetation type, provided further proof for a needed revision of the vegetation in the model, as there is a reasonable correlation between this parameter and the Tmax mean errors when using the ESA-CCI vegetation cover (while the same correlation cannot be reproduced with the original model vegetation). Our results support the hypothesis that vegetation cover is one of the main drivers of the LST summertime cold bias in ERA5 over Iberian Peninsula.

Earth observations were used to evaluate the representation of land surface temperature (LST) and vegetation coverage over Iberia in two state-of-the-art land surface models (LSMs) – the European Centre for Medium-Range Weather Forecasts (ECMWF) Carbon-Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (CHTESSEL) and the Météo-France Interaction between Soil Biosphere and Atmosphere model (ISBA) within the SURface EXternalisée modeling platform (SURFEX-ISBA) for the 2004–2015 period. The results showed that the daily maximum LST simulated by CHTESSEL over Iberia was affected by a large cold bias during summer months when compared against the Satellite Application Facility on Land Surface Analysis (LSA-SAF), reaching magnitudes larger than 10 ^∘C over wide portions of central and southwestern Iberia. This error was shown to be tightly linked to a misrepresentation of the vegetation cover. In contrast, SURFEX simulations did not display such a cold bias. We show that this was due to the better representation of vegetation cover in SURFEX, which uses an updated land cover dataset (ECOCLIMAP-II) and an interactive vegetation evolution, representing seasonality. The representation of vegetation over Iberia in CHTESSEL was improved by combining information from the European Space Agency Climate Change Initiative (ESA-CCI) land cover dataset with the Copernicus Global Land Service (CGLS) leaf area index (LAI) and fraction of vegetation coverage (FCOVER). The proposed improvement in vegetation also included a clumping approach that introduces seasonality to the vegetation cover. The results showed significant added value, removing the daily maximum LST summer cold bias completely, without reducing the accuracy of the simulated LST, regardless of season or time of the day. The striking performance differences between SURFEX and CHTESSEL were fundamental to guiding the developments in CHTESSEL highlighting the importance of using different models. This work has important implications: first, it takes advantage of LST, a key variable in surface–atmosphere energy and water exchanges, which is closely related to satellite top-of-atmosphere observations, to improve the model's representation of land surface processes. Second, CHTESSEL is the land surface model employed by ECMWF in the production of their weather forecasts and reanalysis; hence systematic errors in land surface variables and fluxes are then propagated into those products. Indeed, we showed that the summer daily maximum LST cold bias over Iberia in CHTESSEL is present in the widely used ECMWF fifth-generation reanalysis (ERA5). Finally, our results provided hints about the interaction between vegetation land–atmosphere exchanges, highlighting the relevance of the vegetation cover and respective seasonality in representing land surface temperature in both CHTESSEL and SURFEX. As a whole, this work demonstrated the added value of using multiple earth observation products for constraining and improving weather and climate simulations.

In this study, we show that limitations in the representation of land cover and vegetation seasonality in the European Centre for Medium‐Range Weather Forecasting (ECMWF) model are partially responsible for large biases (up to ∼10°C, either positive or negative depending on the region) on the simulated daily maximum land surface temperature (LST) with respect to satellite Earth Observations (EOs) products from the Land Surface Analysis Satellite Application Facility. The error patterns were coherent in offline land‐surface and coupled land‐atmosphere simulations, and in ECMWF's latest generation reanalysis (ERA5). Subsequently, we updated the ECMWF model's land cover characterization leveraging on state‐of‐the‐art EOs—the European Space Agency Climate Change Initiative land cover data set and the Copernicus Global Land Services leaf area index. Additionally, we tested a clumping parameterization, introducing seasonality to the effective low vegetation coverage. The updates reduced the overall daily maximum LST bias and unbiased root‐mean‐squared errors. In contrast, the implemented updates had a neutral impact on daily minimum LST. Our results also highlighted the complex regional heterogeneities in the atmospheric sensitivity to land cover and vegetation changes, particularly with issues emerging over eastern Brazil and northeastern Asia. These issues called for a re‐calibration of model parameters (e.g., minimum stomatal resistance, roughness length, rooting depth), along with a revision of several model assumptions (e.g., snow shading by high vegetation).

The recent ECMWF (European Centre of Medium‐Range Weather Forecasts) reanalysis product (ERA5) is a 3D‐atmospheric dataset of unparalleled value to advance the characterization and understanding of the main global coastal low‐level jet (CLLJ) systems, due to its finer resolution (horizontal and vertical) and hourly output. Regional climate studies have revealed the added value of higher spatial and temporal resolutions of models and reanalyses for the representation of CLLJs, which play an important role in the definition of the regional climate along the Eastern Boundary Current Systems. ERA5 reveals an improvement in the representation of the wind speed and temperature vertical profiles within the boundary layer in CLLJ regions. This improvement enables a better representation of CLLJs and therefore a new view on their features. ERA5 show larger CLLJ frequencies of occurrence and larger wind speed intensities, when compared with previous reanalysis. The maximum frequency of occurrence ranges from 20% in the West Australian and 73% in the Oman CLLJs, in the summer season, which constitutes an increase of 10% compared to ERA‐Interim. Moreover, a shift for higher wind speeds is also found, where the maximum wind speed approach 30 m·s⁻¹. Typically, CLLJs are found below 500 m above sea level, where the finer vertical discretization results clearly in a better description of the CLLJ peak height. Regarding the diurnal cycles of CLLJs frequency of occurrence, ERA5 exposes higher peaks and larger amplitudes, where the maximum hourly frequency is about 38% in the Oman CLLJ and the diurnal amplitude can vary between 7 and 14%. The diurnal maximum and minimum frequency of CLLJ occurrence can be found between 1900 and 2200 UTC, and between 1000 and 1400 LST, respectively. The momentum and temperature budget profiles reveal significant differences between CLLJ regions and emphasize the relevance of local processes in its occurrences.