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Cities concentrate people, wealth, emissions, and infrastructure, thus representing a challenge and an opportunity for climate change mitigation and adaptation. This urgently demands for accurate urban climate projections to help organizations and individuals to make climate-smart decisions. However, most of the large ensembles of global and regional climate model simulations do not include sophisticated urban parameterizations (e.g., EURO-CORDEX; CMIP5/6). Here, we explore this shortcoming in ERA5 (the latest generation reanalysis from the European Centre for Medium-Range Weather Forecasts) and in a simulation with the SURFEX (Surface Externalisée) land surface model employing the widely used bulk bare rock approach. The city of Paris is considered as a case study. Subsequently, we apply a more complex urban scheme – SURFEX coupled to the Town Energy Balance (TEB) urban canopy model to assess its benefits on characterizing the Paris urban climate. Both simulations and ERA5 were compared to the LSA SAF (Satellite Application Facility on Land Surface Analysis) land surface temperature product to evaluate the simulation of Parisian surface urban heat island (SUHI). Our results show a significant added value of SURFEX-TEB in reproducing the SUHI during the daytime and the UHI during both the daytime and nighttime (with overall reductions in the bias and root mean square error and improvements in the representation of the statistics of the SUHI/UHI displayed by the Perkins skill score or S score). The improvement in the simulated SUHI is lower during the nighttime due to the lack of land–atmosphere feedbacks in the proposed offline framework. Nonetheless, the offline SURFEX-TEB framework applied here clearly demonstrates the added value of using more comprehensive parameterization schemes to simulate the urban climate and, therefore, allowing the improvement of urban climate projections.

The Antarctic plateau, characterized by cold and dry weather conditions with very little precipitation, is mostly covered by snow at the surface. This paper describes an intercomparison of snow models, of varying complexity, used for numerical weather prediction or academic research. The results of offline numerical simulations, carried out during 15 d in 2009, on a single site on the Antarctic plateau, show that the simplest models are able to reproduce the surface temperature as well as the most complex models provided that their surface parameters are well chosen. Furthermore, it is shown that the diversity of the surface parameters of the models strongly impacts the numerical simulations, in particular the temporal variability of the surface temperature and the components of the surface energy balance. The models tend to overestimate the surface temperature by 2–5 K at night and underestimate it by 2 K during the day. The observed and simulated turbulent latent heat fluxes are small, of the order of a few W m−2, with a tendency to underestimate, while the sensible heat fluxes are in general too intense at night as well as during the day. The surface temperature errors are consistent with too large a magnitude of sensible heat fluxes during the day and night. Finally, it is shown that the most complex multilayer models are able to reproduce well the propagation of the daily diurnal wave, and that the snow temperature profiles in the snowpack are very close to the measurements carried out on site.

Land surface plays a fundamental role in the earth system, mediating the water, energy and carbon fluxes between the land and the atmosphere. The land surface physical and biophysical processes act on time scales ranging from sub-daily to decades with relevant impacts from weather forecasts to climate change. However, there are very few available in-situ observations of land surface state and fluxes extending for several years to decades, limiting an integrated validation of the models on the different time scales. The long time series of Cabauw (Netherlands) observations provides a unique opportunity to evaluate land surface processes and their representation in land surface model at time scales ranging from sub-diurnal to interannual. In this study, we take advantage of the uniqueness of Cabauw observational record to investigate the performance of the ECMWF land surface model ECLand for the period 2001-2020 (20 years). Emphasis is given to the summer season and to evaporation and evaporative fraction. An idealized simulation without canopy resistance is performed along with other model configurations with changes to the constraints of canopy resistance (soil moisture availability and atmospheric humidity deficit) and the vertical discretization of the soil layers.Observational uncertainties impact the surface energy budget closure. For example, the model shows a large overestimation of the ground heat flux diurnal cycle. However, part of this can be attributed to observational uncertainties associated with the sinking of the temperature sensors. The default configuration of ECLand shows an underestimation of latent heat and evaporative fraction, which can be partially attributed to the model’s representation of canopy resistance. The increased vertical discretization of the soil layers has a neutral impact on the simulated turbulent fluxes, showing an improved representation of near-surface soil temperature. Our results show limitations in the representation of the summer interannual variability of the turbulent fluxes. These are associated with the representation of extreme events (droughts) and are not fully addressed in any of the model configurations tested. These results suggest that other processes relevant to the representation of evaporation in dryness stress conditions need to be further investigated.This work was developed in the framework of the project NextGEMS funded through the European Union’s Horizon 2020 research and innovation program under the grant agreement number 101003470. Luis Frois was funded by the FCT Grant 2020.08478.BD. The authors also acknowledge the financial support of the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC) – UIDB/50019/2020- IDL.

We present Multi-Source Weather (MSWX), a seamless global gridded near-surface meteorological product featuring a high 3-hourly 0.1° resolution, near-real-time updates (∼3-h latency), and bias-corrected medium-range (up to 10 days) and long-range (up to 7 months) forecast ensembles. The product includes 10 meteorological variables: precipitation, air temperature, daily minimum and maximum air temperature, surface pressure, relative and specific humidity, wind speed, and downward shortwave and longwave radiation. The historical part of the record starts 1 January 1979 and is based on ERA5 data bias corrected and downscaled using high-resolution reference climatologies. The data extension to within ∼3 h of real time is based on analysis data from GDAS. The 30-member medium-range forecast ensemble is based on GEFS and updated daily. Finally, the 51-member long-range forecast ensemble is based on SEAS5 and updated monthly. The near-real-time and forecast data are statistically harmonized using running-mean and cumulative distribution function-matching approaches to obtain a seamless record covering 1 January 1979 to 7 months from now. MSWX presents new and unique opportunities for hydrological modeling, climate analysis, impact studies, and monitoring and forecasting of droughts, floods, and heatwaves (within the bounds of the caveats and limitations discussed herein). The product is available at www.gloh2o.org/mswx.

The daily maximum and minimum temperatures are among the most relevant meteorological variables in weather forecasts and climate monitoring. Their spatial and temporal evolution from synoptical to decadal scales are driven by numerous physical processes and climate feedbacks. Despite the significant improvements in weather forecasting over the last decades, forecasts of daily temperature extremes are still hampered by systematic errors. In this work we perform an integrated evaluation of the daily temperature extremes of the (i) ECMWF ERA5 reanalyses and (ii) ECMWF operational weather forecasts. The observations for the evaluation are taken from the Global Historical Climatology Network (GHCN) addressing: (i) the long-term assessment of the analysis produced by the ERA5 reanalysis, comprising a 40-year period (from 1980 to 2019); and (ii) the assessment of the ECMWF operational forecasts for a 5- year period (from 2017 to 2021). The evaluation carried out is global, however considering the GHCN station distribution and temporal availability, particular focus was given to four regions: Europe, Australia, East and West United States. The results identify a general underestimation of the daily maximum and overestimation of the daily minimum temperatures in both ERA5 analysis and operational forecasts, highlighting a known limitation of the ECMWF model in underestimating the diurnal temperature range. Our results also indicate a reduction of the errors in ERA5 when comparing the latest decade with the 1980’s, which is likely to be associated with an enhanced quality of the analysis due to a higher constrain emerging from the satellite data. The ERA5 analysis outperforms 1 day-ahead weather forecasts, which show some degree of improvement in the considered 5-year period, being associated with model upgrades. This work was developed in the framework of the CoCO2 project. CoCO2 project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 958927.

We present Multi-Source Weather (MSWX), a seamless global gridded near-surface meteorological product featuring a high 3-hourly 0.1° resolution, near-real-time updates (∼3-h latency), and bias-corrected medium-range (up to 10 days) and long-range (up to 7 months) forecast ensembles. The product includes 10 meteorological variables: precipitation, air temperature, daily minimum and maximum air temperature, surface pressure, relative and specific humidity, wind speed, and downward shortwave and longwave radiation. The historical part of the record starts 1 January 1979 and is based on ERA5 data bias corrected and downscaled using high-resolution reference climatologies. The data extension to within ∼3 h of real time is based on analysis data from GDAS. The 30-member medium-range forecast ensemble is based on GEFS and updated daily. Finally, the 51-member long-range forecast ensemble is based on SEAS5 and updated monthly. The near-real-time and forecast data are statistically harmonized using running-mean and cumulative distribution function-matching approaches to obtain a seamless record covering 1 January 1979 to 7 months from now. MSWX presents new and unique opportunities for hydrological modeling, climate analysis, impact studies, and monitoring and forecasting of droughts, floods, and heatwaves (within the bounds of the caveats and limitations discussed herein). The product is available at www.gloh2o.org/mswx.

Land Surface Temperature (LST) and Surface Net Radiation (SNR) are vital inputs for many land surface and hydrological models. However, current remote sensing datasets of these variables come mostly at coarse resolutions. Although high-resolution LST and SNR retrievals are available, they have large gaps due to cloud-cover that hinder their use as input in models. Here, we present a downscaled and continuous daily LST and SNR product across Europe for 2018–2019. The LST product is based on all-sky LST retrievals from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the geostationary Meteosat Second Generation (MSG) satellite, and clear-sky LST retrievals from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the polar-orbiting Sentinel 3 satellites. The product combines the medium spatial (approx. 5–7 km) but high temporal (30 minute) resolution, gap-free data from MSG, with the low temporal (2–3 days) but high spatial (1 km) resolution of the Sentinel 3 LST retrievals. The resulting 1 km and daily LST dataset is based on an hourly merging of both datasets through bias-correction and Kalman Filter assimilation. Longwave outgoing radiation is computed from the merged LST product in combination with MSG-based emissivity data. Shortwave outgoing radiation is computed from the incoming shortwave radiation from MSG and downscaled albedos using 1 km PROBA-V data. MSG incoming shortwave and longwave radiation and the outgoing radiation components at 1 km spatial resolution are used together to compute the final daily SNR dataset in a consistent manner. Validation results indicate an improvement of the root mean squared error by ca. 8 % with a substantial increase in spatial detail compared to the original MSG product. The resulting pan-European LST and SNR dataset can be used for hydrological modelling and as input to models dedicated to estimating evaporation and surface turbulent heat fluxes and will be regularly updated in the future.

Twenty-seven models participated in the Earth System Model–Snow Model Intercomparison Project (ESM-SnowMIP), the most data-rich MIP dedicated to snow modeling. Our findings do not support the hypothesis advanced by previous snow MIPs: evaluating models against more variables and providing evaluation datasets extended temporally and spatially does not facilitate identification of key new processes requiring improvement to model snow mass and energy budgets, even at point scales. In fact, the same modeling issues identified by previous snow MIPs arose: albedo is a major source of uncertainty, surface exchange parameterizations are problematic, and individual model performance is inconsistent. This lack of progress is attributed partly to the large number of human errors that led to anomalous model behavior and to numerous resubmissions. It is unclear how widespread such errors are in our field and others; dedicated time and resources will be needed to tackle this issue to prevent highly sophisticated models and their research outputs from being vulnerable because of avoidable human mistakes. The design of and the data available to successive snow MIPs were also questioned. Evaluation of models against bulk snow properties was found to be sufficient for some but inappropriate for more complex snow models whose skills at simulating internal snow properties remained untested. Discussions between the authors of this paper on the purpose of MIPs revealed varied, and sometimes contradictory, motivations behind their participation. These findings started a collaborative effort to adapt future snow MIPs to respond to the diverse needs of the community.

The estimation of downward long-wave radiation (DLR) at the surface is very important for the understanding of the Earth’s radiative budget with implications in surface–atmosphere exchanges, climate variability, and global warming. Theoretical radiative transfer and observationally based studies identify the crucial role of clouds in modulating the temporal and spatial variability of DLR. In this study, a new machine learning algorithm that uses multivariate adaptive regression splines (MARS) and the combination of near-surface meteorological data with satellite cloud information is proposed. The new algorithm is compared with the current operational formulation used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Land Surface Analysis (LSA-SAF). Both algorithms use near-surface temperature and dewpoint temperature along with total column water vapor from the latest European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis ERA5 and satellite cloud information from the Meteosat Second Generation. The algorithms are trained and validated using both ECMWF-ERA5 and DLR acquired from 23 ground stations as part of the Baseline Surface Radiation Network (BSRN) and the Atmospheric Radiation Measurement (ARM) user facility. Results show that the MARS algorithm generally improves DLR estimation in comparison with other model estimates, particularly when trained with observations. When considering all the validation data, root mean square errors (RMSEs) of 18.76, 23.55, and 22.08 W·m−2 are obtained for MARS, operational LSA-SAF, and ERA5, respectively. The added value of using the satellite cloud information is accessed by comparing with estimates driven by ERA5 total cloud cover, showing an increase of 17% of the RMSE. The consistency of MARS estimate is also tested against an independent dataset of 52 ground stations (from FLUXNET2015), further supporting the good performance of the proposed model.

Urban areas concentrate population, wealth, infrastructures, and emissions thus playing a major role on climate change mitigation and adaptation. Therefore, an urgent need for city future climate projections exists to assist stakeholders, organizations and individuals promoting climate mitigation and adaptation measures. Nevertheless, most of the state-of-the-art global and regional climate models have a simplified representation of (or completely neglect) urban climate processes. In the current work, the city of Paris is used as a case study to reveal the shortcomings of the fifth-generation reanalysis from the European Centre for Medium-Range Weather Forecasts (ERA5) and of simulations employing the widely used bulk bare rock approach to urban climate simulation. Afterwards, based on the hourly resolution output of ERA5 and on the Satellite Application Facility Land Surface Analysis (LSA-SAF) land surface temperature product, we show the added value of employing the SURFEX land-surface model coupled to Town Energy Balance (TEB) urban canopy model in simulating the Parisian Surface Urban Heat Island (SUHI) during daytime and the urban heat island during both daytime and night-time. The results display significant the SURFEX-TEB gains in representing the observed daytime and night-time urban heat island effect. SURFEX-TEB diminishes the annual average bias magnitude in 0.5ºC for daytime and 1.5ºC for night-time, when compared to ERA5 and to bare rock approach. Also, SURFEX-TEB revealed an overall better performance in reproducing the observed daytime SUHI. Finally, the offline SURFEX-TEB framework applied here reveals its improved adequacy to simulate the city climate, which is crucial to build city climate future projections and efficient mitigation and adaptation strategies.Acknowledgements. The authors would like to acknowledge the financial support FCT through project UIDB/50019/2020 – Instituto Dom Luiz.

Climate change is likely to impact the balance of worldwide food exchange networks and food security. Hence, the use of seasonal forecasts of precipitation and temperature may be regarded as essential for stakeholders to perform timely choices concerning the strategies required to maximize the expected cereal yield outcomes in the harvest period. The availability and ease-of-use of the seasonal forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) system 5 (SEAS5) may be an important asset to help implement these strategies by decision makers. Nevertheless, uncertainties and reduced skill may hinder the use of such forecasts for numerous applications. Thus, this work intends to analyse the added value of using dynamical forecasts when compared to using persistent anomalies of climate conditions, with the aim of predicting the production of wheat and barley yields in Iberia. First, empirical models involving annual wheat and barley yields in Spain and monthly values of precipitation and temperature are developed with ECMWF ERA5 reanalysis. Then, dynamical and persistence forecasts are issued at different lead times, and the skill of the forecasted yield is verified through different metrics. Results presented here show that wheat and barley yield anomaly forecasts (dynamical and persistent) start to gain skill later in the season (e.g., April) and show that the added value of using the SEAS5 forecast as an alternative to persistence varies between 6 and 16 %, with better results in the southern Spain regions.The authors would like to acknowledge the financial support FCT through project UIDB/50019/2020 – Instituto Dom Luiz.

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.

A large fraction of extreme precipitation and flood events across western Europe are triggered by atmospheric rivers (ARs). The association between ARs and extreme precipitation days over the Iberian Peninsula has been well documented for western river basins. Since ARs are often associated with high impact weather, it is important to study their medium-range predictability. Here we perform such an assessment using the ECMWF ensemble forecasts up to 15 d for events where ARs made landfall in the western Iberian Peninsula during the winters spanning between 2012–2013 and 2015–2016. Vertically integrated horizontal water vapor transport (IVT) and precipitation from the 51 ensemble members of the ECMWF Integrated Forecasting System (IFS) ensemble (ENS) were processed over a domain including western Europe and the contiguous North Atlantic Ocean. Metrics concerning AR location, intensity, and orientation were computed, in order to compare the predictive skill (for different prediction lead times) of IVT and precipitation. We considered several regional boxes over western Iberia, where the presence of ARs is detected in analysis/forecasts, enabling the construction of contingency tables and probabilistic evaluation for further objective verification of forecast accuracy. Our results indicate that the ensemble forecasts have skill in detecting upcoming AR events, which can be particularly useful to better predict potential hydrometeorological extremes. We also characterized how the ENS dispersion and confidence curves change with increasing forecast lead times for each sub-domain. The probabilistic evaluation, using receiver operating characteristic (ROC) analysis, shows that for short lead times precipitation forecasts are more accurate than IVT forecasts, while for longer lead times this reverses (∼10 d). Furthermore, we show that this reversal occurs for shorter lead times in areas where the AR contribution is more relevant for winter precipitation totals (e.g., northwestern Iberia).

Cities concentrate people, wealth, emissions, and infrastructures, thus representing a challenge and an opportunity for climate change mitigation and adaptation. This places an urgent demand for accurate urban climate projections to help organizations and individuals making climate smart-decisions. However, most of the state-of-the-art global and regional climate models have an oversimplified representation of (or completely neglect) urban climate processes. Here, we use the city of Paris as a case study to show that this is the case for the fifth (and latest) generation reanalysis from the European Centre for Medium-Range Weather Forecasts (ERA5) and for simulations employing the widely used bulk bare rock approach to urban climate parameterization. Subsequently, we leveraged on the hourly resolution of ERA5 and the Satellite Application Facility Land Surface Analysis (LSA-SAF) land surface temperature product to demonstrate the significant added value of employing the SURFEX land-surface model coupled to Town Energy Balance (TEB) urban canopy model in simulating the Parisian Surface Urban Heat Island (SUHI) during daytime and the urban heat island during both daytime and nighttime. Our results showed the significant added value of SURFEX-TEB in reproducing the observed daytime and nighttime Parisian urban heat island effect. An annual average bias magnitude reduction of 0.5 °C was observed for daytime and around 1.5 °C for nighttime when compared to ERA5 and bare rock approach. Also, SURFEX-TEB revealed an overall better performance in reproducing the observed daytime SUHI, whilst the added value of SURFEX-TEB was lower during nighttime (but still slightly better than ERA5 and the bare rock approach), due to the lack of land-atmosphere feedbacks in the proposed offline framework. Finally, the offline SURFEX-TEB framework applied here demonstrates the ability to simulate the urban climate, which is an asset to build urban climate projections that allow the development of mitigation and adaptation strategies.

This study focuses on the Iberian Peninsula and investigates the propagation of precipitation uncertainty, and its interaction with hydrologic modeling, in global water resource reanalysis. Analysis is based on ensemble hydrologic simulations for a period spanning 11 years (2000–2010). To simulate the hydrological variables of surface runoff, subsurface runoff, and evapotranspiration, we used four land surface models (LSMs) – JULES (Joint UK Land Environment Simulator), ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems), SURFEX (Surface Externalisée), and HTESSEL (Hydrology – Tiled European Centre for Medium-Range Weather Forecasts – ECMWF – Scheme for Surface Exchanges over Land) – and one global hydrological model, WaterGAP3 (Water – a Global Assessment and Prognosis). Simulations were carried out for five precipitation products – CMORPH (the Climate Prediction Center Morphing technique of the National Oceanic and Atmospheric Administration, or NOAA), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), 3B42V(7), ECMWF reanalysis, and a machine-learning-based blended product. As a reference, we used a ground-based observation-driven precipitation dataset, named SAFRAN, available at 5 km, 1 h resolution. We present relative performances of hydrologic variables for the different multi-model and multi-forcing scenarios. Overall, results reveal the complexity of the interaction between precipitation characteristics and different modeling schemes and show that uncertainties in the model simulations are attributed to both uncertainty in precipitation forcing and the model structure. Surface runoff is strongly sensitive to precipitation uncertainty, and the degree of sensitivity depends significantly on the runoff generation scheme of each model examined. Evapotranspiration fluxes are comparatively less sensitive for this study region. Finally, our results suggest that there is no single model–forcing combination that can outperform all others consistently for all variables examined and thus reinforce the fact that there are significant benefits to exploring different model structures as part of the overall modeling approaches used for water resource applications.

Subseasonal forecasts lie between medium-range and seasonal time scales with an emerging attention due to the relevance in society and by the scientific challenges involved. This study aims to (i) evaluate the development of systematic errors with lead time in the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts of surface-related variables during late spring and summer, and (ii) investigate potential relationships between the systematic errors and predictive skill. The evaluation is performed over the northern hemisphere midlatitudes, focusing on several regions with different climate characteristics. The results indicate five key bias patterns: (i) cold bias of daily maximum temperature (mx2t) in the April-May forecasts at all lead times in most regions; (ii) central North America with a warm bias mostly in the daily minimum temperature (mn2t); (iii) East of Caspian Sea region with a warm and dry bias; (iv) Western and Mediterranean Europe with a cold bias in mn2t mainly in April-May forecasts and (v) continental Europe with a cold bias in the mx2t and warm bias of mn2t in the June-July forecasts. We also found substantial deviations of soil moisture and terrestrial water storage variation in most regions compared to ECMWF ERA5 reanalysis. Despite the large differences in the systematic error characteristics among the different regions, there is little relation to the skill of the subseasonal forecasts. The systematic temperature biases require further attention from model developers as diurnal cycle improvements could enhance some of the potential predictability coming from the long-memory effect of soil moisture.

Framed within the Copernicus Climate Change Service (C3S) of the European Commission, the European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the fifth generation of European ReAnalysis (ERA5), hereafter referred to as ERA5-Land. Once completed, the period covered will span from 1950 to the present, with continuous updates to support land monitoring applications. ERA5-Land describes the evolution of the water and energy cycles over land in a consistent manner over the production period, which, among others, could be used to analyse trends and anomalies. This is achieved through global high-resolution numerical integrations of the ECMWF land surface model driven by the downscaled meteorological forcing from the ERA5 climate reanalysis, including an elevation correction for the thermodynamic near-surface state. ERA5-Land shares with ERA5 most of the parameterizations that guarantees the use of the state-of-the-art land surface modelling applied to numerical weather prediction (NWP) models. A main advantage of ERA5-Land compared to ERA5 and the older ERA-Interim is the horizontal resolution, which is enhanced globally to 9 km compared to 31 km (ERA5) or 80 km (ERA-Interim), whereas the temporal resolution is hourly as in ERA5. Evaluation against independent in situ observations and global model or satellite-based reference datasets shows the added value of ERA5-Land in the description of the hydrological cycle, in particular with enhanced soil moisture and lake description, and an overall better agreement of river discharge estimations with available observations. However, ERA5-Land snow depth fields present a mixed performance when compared to those of ERA5, depending on geographical location and altitude. The description of the energy cycle shows comparable results with ERA5. Nevertheless, ERA5-Land reduces the global averaged root mean square error of the skin temperature, taking as reference MODIS data, mainly due to the contribution of coastal points where spatial resolution is important. Since January 2020, the ERA5-Land period available has extended from January 1981 to the near present, with a 2- to 3-month delay with respect to real time. The segment prior to 1981 is in production, aiming for a release of the whole dataset in summer/autumn 2021. The high spatial and temporal resolution of ERA5-Land, its extended period, and the consistency of the fields produced makes it a valuable dataset to support hydrological studies, to initialize NWP and climate models, and to support diverse applications dealing with water resource, land, and environmental management. The full ERA5-Land hourly (Muñoz-Sabater, 2019a) and monthly (Muñoz-Sabater, 2019b) averaged datasets presented in this paper are available through the C3S Climate Data Store at https://doi.org/10.24381/cds.e2161bac and https://doi.org/10.24381/cds.68d2bb30, respectively.

With increasingly higher spatial resolution and a broader applications, the importance of soil representation (e.g. soil depth, vertical discretisation, vegetation rooting) within land surface models is enhanced. Those modelling choices actually affects the way land surfaces store and regulate water, energy and also carbon fluxes. Heat and water vapour fluxes towards the atmosphere and deeper soil, exhibit variations spanning a range of time scales from minutes to months in the coupled land-atmosphere system. This is further modulated by the vertical roots' distribution, and soil moisture stress function, which control evapotranspiration under soil moisture stress conditions. Currently in the ECMWF land Surface Scheme the soil column is represented by a fixed 4 layers configuration with a total of approximately 3m depth.In the present study we explore new configurations with increased soil depth (up to 8m) and higher vertical discretisation (up to 10 layers) including a dissociation between the treatment of water and heat fluxes. Associated with the soil vertical resolution, the vertical distribution of roots is also investigated. A new scheme that assumes a uniform root distribution with an associated maximum rooting depth is explored. The impact of these new configurations is assessed through surface offline simulations driven by the ERA5 meteorological forcing against in-situ and global products of energy, water and carbon fluxes with a particular focus on the diurnal cycle and extreme events in recent years.

As the global climate is warming, with important regional differences, there is a growing need to (i) better understand whether and how internal variability controls the regional warming trends, and (ii) to identify the regions in which both the trends and the superimposed interannual variability are predictable. In this study we investigate trends, variability and predictive skill of the upper ocean heat content in the North Atlantic basin. This region is a source of decadal variability, in which internal ocean processes can locally modulate the global warming trends and add additional prediction skill. The analysis is focused on the period 1970-2014, and combines the study of an ensemble of ocean reanalyses, with two sets of CMIP6 experiments performed with the Earth system model EC-Earth3: (i) a 10-member historical ensemble; and (ii) an initialized 10-member retrospective decadal prediction system. External forcings are found to be important for the development of the regional trends, but on their own are unable to reproduce the exact geographical pattern. Our results also show that not all regions in the North Atlantic are equally predictable, which is explained by different contributions of the forcings and internal climate variability. While high levels of predictive skill in regions like the Eastern Subpolar North Atlantic, or the Irminger and Iceland Seas are clearly enabled by initialization, with a negligible influence of the external forcings, skill in others areas like the Subtropical North Atlantic, or the Gulf Stream extension mostly comes from the externally forced trends. The Labrador Sea is a particular case where predictive skill has both an external and internal origin. Large observational and modeling uncertainties affect the Central Subpolar North Atlantic, the only region exhibiting a cooling during the study period, uncertainties that might explain its very poor predictive skill. We would like to acknowledge the financial support from FCT through projects FCT-UIDB/50019/2020 and PD/BD/142785/2018.

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 similar to 10 degrees 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 surface-atmosphere turbulent exchanges couple the water, energy and carbon budgets in the Earth system. The biosphere plays an important role in the evaporation process, and vegetation related parameters such as the leaf area index (LAI), vertical root distribution and stomatal resistance are poorly constrained due to sparse observations at the spatio-temporal scales at which land surface models (LSMs) operate. In this study, we use the Carbon Hydrology Tiled European Center for Medium-Range Weather Forecasts (ECMWF) Scheme for Surface Exchanges over Land (CHTESSEL) model and investigate the sensitivity of the simulated turbulent fluxes to these vegetation related parameters. Observed data from 17 FLUXNET towers were used to force and evaluate model simulations with different vegetation parameter configurations. The replacement of the current LAI climatology used by CHTESSEL, by a new high-resolution climatology, representative of the station’s location, has a small impact on the simulated fluxes. Instead, a revision of the root profile considering a uniform root distribution reduces the underestimation of evaporation during water stress conditions. Despite the limitations of using only one model and a limited number of stations, our results highlight the relevance of root distribution in controlling soil moisture stress, which is likely to be applicable to other LSMs.

In this paper, we present and evaluate the skill of an EC-Earth3.3 decadal prediction system contributing to the Decadal Climate Prediction Project – Component A (DCPP-A). This prediction system is capable of skilfully simulating past global mean surface temperature variations at interannual and decadal forecast times as well as the local surface temperature in regions such as the tropical Atlantic, the Indian Ocean and most of the continental areas, although most of the skill comes from the representation of the external radiative forcings. A benefit of initialization in the predictive skill is evident in some areas of the tropical Pacific and North Atlantic oceans in the first forecast years, an added value that is mostly confined to the south-east tropical Pacific and the eastern subpolar North Atlantic at the longest forecast times (6–10 years). The central subpolar North Atlantic shows poor predictive skill and a detrimental effect of initialization that leads to a quick collapse in Labrador Sea convection, followed by a weakening of the Atlantic Meridional Overturning Circulation (AMOC) and excessive local sea ice growth. The shutdown in Labrador Sea convection responds to a gradual increase in the local density stratification in the first years of the forecast, ultimately related to the different paces at which surface and subsurface temperature and salinity drift towards their preferred mean state. This transition happens rapidly at the surface and more slowly in the subsurface, where, by the 10th forecast year, the model is still far from the typical mean states in the corresponding ensemble of historical simulations with EC-Earth3. Thus, our study highlights the Labrador Sea as a region that can be sensitive to full-field initialization and hamper the final prediction skill, a problem that can be alleviated by improving the regional model biases through model development and by identifying more optimal initialization strategies. The work in this paper was supported by the European Commission H2020 projects EUCP (grant no. 776613), APPLICATE (grant no. 727862), INTAROS (grant no. 727890) and PRIMAVERA (grant no. 641727); a Spanish project funded by the Spanish Ministry of Economy, Industry and Competitiveness (CLINSA, grant no. CGL2017-85791-R); a FRS-FNRS/FWO-funded Belgian project (PARAMOUR, grant no. EOS-30454083); and an ESA contract (grant no. CMUG-CCI3-TECHPROP). The climate simulations analysed in the paper were performed using the internal computing resources available at MareNostrum and additional resources from PRACE (HiResNTCP, project 3: grant no. 2017174177) and the Red Española de Supercomputación (AECT-2019-2-0003 and AECT-2019-3-0006 projects) as well as technical support provided by the Barcelona Supercomputing Center. In addition, several co-authors have been supported by personal grants: Yohan Ruprich-Robert, Etienne Tourigny and Simon Wild received funding from the European Union Horizon 2020 research and innovation programme (grant agreement nos. 800154, 748750 and 754433 respectively); Ivana Cvijanovic was supported by Generalitat de Catalunya (Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement) through the Beatriu de Pinós programme; Juan Acosta-Navarro was supported by the Spanish Ministry of Science, Innovation and Universities through a Juan de la Cierva personal grant (grant no. FJCI-2017-34027); Rubén Cruz-García was funded by the Spanish Ministry of Education, Culture and Sports with an FPU grant (grant no. FPU15/01511); and Markus Donat and Pablo Ortega were funded by the Spanish Ministry of Economy, Industry and Competitiveness through the Ramon y Cajal grants RYC-2017-22964 and RYC-2017-22772. We also want to thank Panos Athanasidis, Stephen Yeager and Gerald Meehl for their very helpful comments when reviewing the paper.

In recent years a strong relationship has been found between Atmospheric Rivers (ARs) and extreme precipitation and floods across western Europe, with some regions having 8 of their top 10 annual maxima precipitation events related to ARs. In the case of the Iberian Peninsula, the association between ARs and extreme precipitation days has also been well documented, particularly for western Iberia river basins.Since ARs are often associated with high impact weather, it is important to study their medium-range predictability. Here we perform such an assessment using the ECMWF ensemble forecasts up to 15 days, for events that made landfall in western Iberian Peninsula during the winters spanning between 2012/2013 and 2015/16. IVT and precipitation from the 51 ensemble members of the ECMWF Integrated Forecasting System (IFS) ensemble (ENS) were processed over a domain including western Europe and contiguous North Atlantic Ocean.Metrics concerning the ARs location, intensity and orientation were computed, in order to compare the predictive skill (for different prediction lead times) of IVT and precipitation analyses in the IFS. We considered several regional boxes over Western Iberia, where the presence of ARs is detected in analysis/forecasts, enabling the construction of contingency tables and probabilistic evaluation for further objective verification of forecast accuracy. Our results indicate that the ENS forecasts have skill to detect upcoming ARs events, which can be particularly useful to improve the prediction of associated hydrometeorological extremes. We also characterized how the ENS dispersion and confidence curves change with increasing forecast lead times for each sub-domain. We employed the standard ROC analysis to evaluate the probabilistic component of these predictions showing that for short lead times precipitation forecasts are more accurate than IVT forecasts, while for longer lead times this result is reversed (~10 days). Furthermore, we show that this reversal occurs at shorter lead times in areas where the ARs contribution is more relevant for winter precipitation totals (e.g. northwestern Iberia). AcknowledgementsThe work done was supported by the project Landslide Early Warning soft technology prototype to improve community resilience and adaptation to environmental change (BeSafeSlide) funded by Fundação para a Ciência e a Tecnologia, Portugal (FCT, PTDC/GES-AMB/30052/2017). A.M.R. was also supported by the Scientific Employment Stimulus 2017 from FCT (CEECIND/00027/2017).

The EUMETSAT Land Surface Analysis Satellite Application Facility (LSA-SAF) now offers a wide range of satellite-derived products for land surface monitoring. The catalogue comprises variables quantifying different terms of the surface energy balance (land surface temperature – LST - and emissivity, downwelling radiative fluxes and turbulent fluxes), as well as several vegetation-related indicators, such as the Leaf Area Index, Fraction of Vegetation Cover, Evapotranspiration, Net Primary Production and Fire Radiative Power. The availability of these datasets, especially taking into account that the time series now span nearly two decades, already allows many interesting applications, overviewed in this presentation.Comparisons of remote sensing data for land surfaces with corresponding model data have already been useful: the standard L2 (clear sky) LST has been used to diagnose a systematic cold bias of ERA5 skin temperature over the Iberian Peninsula. Offline simulations using H-TESSEL revealed that the bias could be alleviated using a more realistic representation of vegetation than what is currently used in ERA5. A recently developed product by LSA SAF allows LST retrievals for all-weather conditions, using a surface energy balance model to provide estimates under cloudy pixels. This product is compared to ERA5-Land skin temperature, showing that despite the increased level of detail of the latter (with respect to ERA5), it is still not representing the former correctly. ERA5 Land skin temperature shows large biases (of more than 10 K) and phase errors (with the satellite LST warming up prior to ERA-Land during the morning and cooling down earlier in the late afternoon). Comparisons of the different terms of the surface energy balance from ERA5-Land and LSA SAF are currently in progress to identify causes of the biases.Another interesting application of LSA SAF products is the study of vegetation recovery over wild fire scars. Five wild fire events over Portugal were analyzed in terms of the long term anomalies introduced by the fire in 3 variables: LST, Albedo and Fraction of Vegetation Cover (all provided by LSA SAF). Results suggest that albedo returns to close-to-normal conditions in less than a year, while LST anomalies last much longer. Finally, trends in the land-ocean thermal contrast were evaluated over Western Iberia and Northwest Africa (due to its importance in generating coastal mesoscale circulations). The study used long time series from 1) satellite – LST from CM-SAF and SST from GHRSST; 2) ERA5 global reanalysis and 3) UERRA regional reanalysis. The results strongly depend on the used dataset and sub-region, with UERRA showing a sharp decrease of the thermal contrast over Iberia, while ERA5 shows a positive trend.These results emphasize the need to improve the representation of surface processes in numerical models, particularly over land surfaces. This presentation shows that datasets such as the ones provided by the LSA SAF are key to such improvements.

Subseasonal forecasts (ranging between 2 weeks and 2 months) have been the subject of attention in many operational weather forecasts centers and by the research community in recent years. This growing attention stems from the value of these forecasts for society and from the scientific challenges involved. The scientific challenges of capturing and representing key processes and teleconnections which are relevant at these scales are significant. One example is temperature extremes associated with weather extremes like heatwaves and droughts that can have severe consequences in nature and human health, among others. Some of the limitations in forecast skill arise from the limits of predictability of the chaotic earth system. Model error is also likely to play a relevant role. In this study, we investigate systematic model biases, their evolution with lead time and potential links with forecast skill.This study assessed the skill and biases of the European Centre for Medium-Range Weather Forecasts (ECMWF) subseasonal forecast in predicting the daily temperature extremes in the Northern Hemisphere. These forecasts are from an experimental setup of ECMWF extended-range forecast system. The forecasts compromise 11 ensemble members with weekly starting dates between 9 April to 30 July extending up to 6 weeks lead with a 20-years hindcast period (1998-2017). The forecasts were performed by the coupled ECMWF systems with TcO199 horizontal resolution (about 50km) in the atmosphere and 1x1 degree ocean. A particular focus is given to Europe and to two other regions that were identified with large systematic errors. The data used in this work consisted of the daily maximum and minimum two-meter temperature, precipitation and other surface fluxes that are aggregated into weekly means and verified against ERA5. The evaluation of systematic biases in daily temperature extremes shows a clear increase with lead time, which is widespread on a hemispheric scale. The spatial patterns of model error growth with lead time are reasonably similar between daily maximum and minimum temperatures. However, the amplitude of the errors is remarkably different with general cold bias of daily maximum and warm bias of daily minimum that consistently grow with forecast lead time. Despite the consistent error growth with lead time, there are clear differences between the forecasts initialized in late Spring (April-May) and those in Summer (June-July). These biases are not fully collocated in two regions in the Northern Hemisphere showing the largest warm temperature biases: Central US and East of Caspian Sea. The warm biases are consistent with an underestimation of precipitation and dry soil moisture, compared to ERA5, but only over the East Caspian region. Forecasts skill assessed via the anomaly correlation shows that the temperature forecasts are skillful up to week 2, with a drop in skill from week 3 onwards. This drop in skill is consistent over all the European domain. Similar results are found for precipitation, but with ACC at week 2 comparable with those of temperature at week 3.

Earth observations were used to evaluate and improve the representation of Land Surface Temperature (LST) and vegetation coverage over Iberia in two state-of-the-art land surface models - the European Center for Medium Range Weather Forecasting (ECMWF) Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) and the Méteo-France Interaction between Soil Biosphere and Atmosphere model (ISBA) within the SURface EXternalisée modelling platform (SURFEX-ISBA) for the 2004-2015 period.The results show that the daily maximum LST simulated by HTESSEL over Iberia is affected by a large cold bias during summer months when compared against the Satellite Application Facility Land Surface Analysis (LSA-SAF), reaching magnitude larger than 10ºC over wide portions of central and southwestern Iberia. This error is shown to be tightly linked to a misrepresentation of the vegetation cover. In contrast, SURFEX simulations did not had such a cold bias. This was due to the better representation of vegetation coverage 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 HTESSEL 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 vegetation includes a clumping approach to introduce seasonality to the vegetation coverage. The results show significant added value, removing the daily maximum LST summer cold bias completely while never reducing the accuracy over all seasons and hours of the day.This work has important implications: First, LST is a key variable in surface-atmosphere energy and water exchanges and, thus, its accurate representation in earth system models is very important. Second, HTESSEL is the land surface model employed by ECMWF in the production of their weather forecasts and reanalysis, hence systematic errors are propagated into these products. Indeed, we show that the summer daily maximum LST cold bias over Iberia in HTESSEL is present in the widely used ECMWF fifth generation reanalysis (ERA5) and fourth generation reanalysis (ERA-Interim). Finally, our results provide hints into the interaction between vegetation land-atmosphere exchanges, highlight the consistent relevance of the vegetation cover and seasonality in representing land surface temperature in both models, and how earth observations play a critical role for constraining and improving weather and climate simulations.

Rivers are a key component of the land hydrological cycle and are crucial in many societal activities and natural hazards. Historically, hydrological modeling has not been tightly associated with numerical weather prediction (NWP) due to the different communities involved, requirements and underlying processes. The increased skill of NWP has led to the uptake of weather forecasts in hydrological models, in particular for flood forecasting. At the same time, developments of Earth System Models (ESM), mainly driven by the climate community have lead to a tight integration of the land hydrological cycle. River discharge is a key quality indicator of the integrated water budget, and its use as a forecast skill metric of NWP has a large potential. Freshwater input to the ocean is also important for the ocean circulation, which becomes increasingly relevant with the current atmosphere-ocean coupling in NWP. Considering all these points, the representation of rivers and floodplains dynamics and their associated impact on inland water evolution is of interest for a wide range of applications currently addressed by global NWP. In this study we present the key technical developments to achieve a 1-way and 2-way coupling between the global hydrodynamic CaMa-Flood model and the land surface component of the European Center for Medium-Range Weather Forecasts (ECMWF) HTESSEL. The models coupling followed a single executable strategy, i.e. avoiding external couplers. A coupling interface was developed for CaMa-Flood that is independent from the driving model, while keeping the stand-alone configuration. The coupling is flexible, allowing both models to run at different spatial resolutions. The implementation allows for a flexible integration of the models and independent development, and can be applied to other models. The current representation of inland water bodies in HTESSEL (lakes) was driven by their impact in NWP, but without the representation of rivers it was not possible to have a consistent water budget. The coupling of CaMa-Flood allows for an integrated earth system model approach. Several options for the 2-way interaction between CaMa-Flood flooded areas in HTESSEL inland water bodies were investigated. Despite the consistent results, several challenges are identified in the representation of inland water bodies, their variability and impact on water cycle.

Snow cover properties have a large impact on the partitioning of surface energy fluxes and thereby on near-surface weather parameters. Snow schemes of intermediate complexity have been widely used for hydrological and climate studies, whereas their impact on typical weather forecast time-scales has received less attention. A new multi-layer snow scheme is implemented in the ECMWF Integrated Forecasting System (IFS) and its impact on snow and 2-metre temperature forecasts is evaluated. The new snow scheme is evaluated offline at well instrumented field sites and compared to the current single-layer scheme. The new scheme largely improves the representation of snow depth for most of the sites considered, reducing the root-mean-square-error averaged over all sites by more than 30%. The improvements are due to a better description of snow density in thick and cold snowpacks, but also due to an improved representation of sporadic melting episodes thanks to the inclusion of a thin top snow layer with a low thermal inertia. The evaluation of coupled 10-day weather forecasts shows an improved representation of snow depth at all lead times, demonstrating a positive impact at the global scale. Regarding the impact on weather parameters, the use of the multi-layer snow scheme improves the simulated daily minimum 2-metre temperature, by decreasing the positive bias and improving the amplitude of the diurnal cycle over snow-covered regions. The analysis indicates that a more realistic representation of snow processes is essential to improve the simulation of low temperature extremes at high latitudes, where snow is a key component of the climate system. The work also highlights that other errors in polar regions still need to be addressed, such as cloud radiative properties, despite the improvements in the responsiveness of snow-covered surfaces with respect to the atmospheric forcing.