Comment on hess-2023-28

Abstract. This paper uses hindcasts (1981–2010) to investigate the sources of skill in seasonal hydrological forecasts for Europe. The hindcasts were produced with WUSHP (Wageningen University Seamless Hydrological Prediction system). Skill was identified in a companion paper. In WUSHP, hydrological processes are simulated by running the Variable Infiltration Capacity (VIC) hydrological model forced with an ensemble of bias-corrected output from the seasonal forecast system 4 (S4) of the European Centre for Medium-Range Weather Forecasts (ECMWF). We first analysed the meteorological forcing. The precipitation forecasts contain considerable skill for the first lead month but hardly any significant skill at longer lead times. Seasonal forecasts of temperature have more skill. Skill in summer temperature is related to climate change and is more or less independent of lead time. Skill in February and March is unrelated to climate change. Different sources of skill in hydro-meteorological variables were isolated with a suite of specific hydrological hindcasts akin to ensemble streamflow prediction (ESP). These hindcasts show that in Europe, initial conditions of soil moisture (SM) form the dominant source of skill in run-off. From April to July, initial conditions of snow contribute significantly to the skill. Some remarkable skill features are due to indirect effects, i.e. skill due to forcing or initial conditions of snow and soil moisture at an earlier stage is stored in the hydrological state (snow and/or soil moisture) of a later stage, which then contributes to persistence of skill. Skill in evapotranspiration (ET) originates mostly in the meteorological forcing. For run-off we also compared the full hindcasts (with S4 forcing) with two types of ESP (or ESP-like) hindcasts (with identical forcing for all years). Beyond the second lead month, the full hindcasts are less skilful than the ESP (or ESP-like) hindcasts, because inter-annual variations in the S4 forcing consist mainly of noise which enhances degradation of the skill.

Drought is among the most severe hydro-meteorological events, and it is expected to become even more critical and recurrent in multiple regions worldwide. The availability of hydrological information months ahead is crucial for developing effective early warning strategies, which often remain focused on meteorological drought, and to support effective adaptation programs and enhanced preparedness. The Copernicus Emergency Management System (CEMS) provides seasonal ensemble forecasts of river discharge based on the LISFLOOD hydrological model simulations, which are driven by seasonal meteorological ensemble forecasts from the seasonal forecast system version 5 (SEAS5) by the European Centre for Medium-Range Weather Forecasts (ECMWF). In this study, we evaluate the performance of these forecasts in detecting hydrological drought in the period 1991–2022 using the ECMWF reanalysis ERA5 as a proxy for the observational reference. The main results show that the skill of the ensemble mean of the Standardized Streamflow Index (SSI) is high at both 1- and 3-month time horizons; lower-skill forecasts (but still informative) can be achieved up to 6 months ahead. The forecast always outperforms trivial ones done by using the latest available state of the reanalysis, with the highest added value, on average, in the Northern Hemisphere. Seasonality in average river discharge is highlighted as one of the main drivers of the differences in skill between seasons. Additionally, the dominant role of inter-annual variability in initial conditions (soil moisture) and meteorological forcings (precipitation) on forecast skill is observed in summer and spring–summer for the former, and in winter and autumn–winter for the latter. Besides the high skill observed with the ensemble mean, the signal-to-noise ratio (SNR) of the ensemble forecast is identified as key metric to operationally assess and communicate forecast reliability.

Abstract. Soil moisture (SM) plays a critical role in the water and energy cycles of the Earth system; consequently, a long-term SM product with high quality is urgently needed. In this study, five SM products, including one microwave remote sensing product – the European Space Agency's Climate Change Initiative (ESA CCI) – and four reanalysis data sets – European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis – Interim (ERA-Interim), National Centers for Environmental Prediction (NCEP), the 20th Century Reanalysis Project from National Oceanic and Atmospheric Administration (NOAA), and the ECMWF Reanalysis 5 (ERA5) – are systematically evaluated using in situ measurements during 1981–2013 in four climate regions at different timescales over the Chinese mainland. The results show that ESA CCI is closest to the observations in terms of both the spatial distributions and magnitude of the monthly SM. All reanalysis products tend to overestimate soil moisture in all regions but have higher correlations than the remote sensing product except in Northwest China. The largest inconsistency is found in southern Northeast China region, with an unbiased root mean square error (ubRMSE) value larger than 0.04. However, all products exhibit certain weaknesses in representing the interannual variation in SM. The largest relative bias of 144.4 % is found for the ERA-Interim SM product under extreme and severe wet conditions in northeastern China, and the lowest relative bias is found for the ESA CCI SM product, with the minimum of 0.48 % under extreme and severe wet conditions in northwestern China. Decomposing mean square errors suggests that the bias terms are the dominant contribution for all products, and the correlation term is large for ESA CCI. As a result, the ESA CCI SM product is a good option for long-term hydrometeorological applications on the Chinese mainland. ERA5 is also a promising product, especially in northern and northwestern China in terms of low bias and high correlation coefficient. This long-term intercomparison study provides clues for SM product enhancement and further hydrological applications.

Assessment of SMOS soil moisture retrieval parameters using tau–omega algorithms for soil moisture deficit estimation

This study aims at stimulating the development of soil moisture data assimilation systems in a direction where they can provide both the necessary control of slow drift in operational NWP applications and support the physical insight in the performance of the land surface component. It addresses four topics concerning the systematic nature of soil moisture data assimilation experiments over Europe during the growing season of 2000 involving the European Centre for Medium-Range Weather Forecasts (ECMWF) model infrastructure. In the first topic the effect of the (spinup related) bias in 40-yr ECMWF Re-Analysis (ERA-40) precipitation on the data assimilation is analyzed. From results averaged over 36 European locations, it appears that about half of the soil moisture increments in the 2000 growing season are attributable to the precipitation bias. A second topic considers a new soil moisture data assimilation system, demonstrated in a coupled single-column model (SCM) setup, where precipitation and radiation are derived from observations instead of from atmospheric model fields. For many of the considered locations in this new system, the accumulated soil moisture increments still exceed the interannual variability estimated from a multiyear offline land surface model run. A third topic examines the soil water budget in response to these systematic increments. For a number of Mediterranean locations the increments successfully increase the surface evaporation, as is expected from the fact that atmospheric moisture deficit information is the key driver of soil moisture adjustment. In many other locations, however, evaporation is constrained by the experimental SCM setup and is hardly affected by the data assimilation. Instead, a major portion of the increments eventually leave the soil as runoff. In the fourth topic observed evaporation is used to evaluate the impact of the data assimilation on the forecast quality. In most cases, the difference between the control and data assimilation runs is considerably smaller than the (positive) difference between any of the simulations and the observations.

Soil moisture is a key state variable in land surface processes. Since field measurements of soil moisture are generally sparse and remote sensing is limited in terms of observation depth, land surface model simulations are usually used to continuously obtain soil moisture data in time and space. Therefore, it is crucial to evaluate the performance of models that simulate soil moisture under various land surface conditions. In this work, we evaluated and compared two land surface models, the Common Land Model version 2014 (CoLM2014) and the Community Land Model Version 5 (CLM5), using in situ soil moisture observations from the Soil Climate Analysis Network (SCAN). The meteorological and soil attribute data used to drive the models were obtained from SCAN station observations, as were the soil moisture data used to validate the simulation results. The validation results revealed that the correlation coefficients between the simulations by CLM5 (0.38) and observations are generally higher than those by CoLM2014 (0.11), especially in shallow soil (0–0.1016 m). The simulation results by CoLM2014 have smaller bias than those by CLM5 . Both models could simulate diurnal and seasonal variations of soil moisture at seven sites, but we found a large bias, which may be due to the two models’ representation of infiltration and lateral flow processes. The bias of the simulated infiltration rate can affect the soil moisture simulation, and the lack of a lateral flow scheme can affect the models’ division of saturated and unsaturated areas within the soil column. The parameterization schemes in land surface models still need to be improved, especially for soil simulations at small scales.

Abstract. Prolonged deficit of soil moisture can result in significant ecosystem and economical losses. General slowdown of vegetation growth and development, withering of foliage cover, reduction of carbon, nutrients and water cycling, increase of fire and insect outbreaks are just a few examples of soil moisture drought impacts. Thus, an early and timely warning via monitoring and forecast could help to prepare for a drought and manage its consequences. In this study, a new version of Global BROOK90, an automated framework to simulate water balance at any location, is presented. The new framework integrates seasonal meteorological forecasts (SEAS5 forecasting system) from European Centre for Medium-Range Weather Forecasts (ECMWF). Here we studied how well the framework can predict the soil moisture drought on a local scale. Twelve small European catchments (from 7 to 115 km2) characterized by various geographical conditions were chosen to reconstruct the 2018–2019 period, when a large-scale prolonged drought was observed in Europe. Setting the ERA5-forced soil moisture simulations as a reference, we analysed how the lead time of the SEAS5 hindcasts influences the quality of the soil moisture predictions under drought and non-drought conditions. It was found that the hindcasted soil moisture fits well with the reference model runs only within the first (in some cases until the second and third) month of lead time. Afterwards, significant deviations up to 50 % of soil water volume were found. Furthermore, within the drought period the SEAS5 hindcast forcings resulted in overestimation of the soil moisture for most of the catchment, indicating an earlier end of a drought period. Finally, it was shown that application of the probabilistic forecast using the ensembles' quantiles to account for the uncertainty of the meteorological input is reasonable only for a lead time of up to 3 months.

In semiarid mountainous regions such as central Asia, runoff from snowmelt often represents the dominant contribution to river flow and freshwater supply during the dry season. The estimation of snow accumulation during the preceding seasons then provides a key to seasonal runoff forecasting with lead times of a few months, and it requires appropriate coverage with surface precipitation and/or snow water equivalent observations. This study tests whether the lack of conventional precipitation and snow observations can be overcome by using model-based precipitation estimates from meteorological data assimilation systems. To this end, a detailed examination is undertaken of the ability of model-assimilated precipitation data to represent the interannual (year-to-year) variations of observed runoff in the Aral Sea basin in central Asia. Precipitation from the 15-yr Re-Analysis (ERA-15) of the European Centre for Medium-Range Weather Forecasts (ECMWF) for the period 1979‐93 is compared against precipitation estimates derived from rain gauge networks, and against the observed natural runoff in the Syrdarya (166 400 km 2) and Amudarya (320 520 km2) basins. It is demonstrated that the ERA-15 dataset is able—despite its low spatial resolution—to describe the seasonal cycle and the larger-scale geographical distribution of precipitation in central Asia. For the Syrdarya basin, it is found that December‐April ERA-15 precipitation correlates well with observed May‐September natural discharge. The correlation coefficient between the two time series amounts to r 5 0.92. It is also demonstrated that ERA-15 precipitation is a better predictor for subsequent runoff than rain gauge‐based precipitation analyses, presumably because of the poor coverage with rain gauge stations. The high correlations suggest that a reliable seasonal runoff forecasting system can be constructed from the statistical relationship between model-assimilated precipitation and subsequent runoff. Cross-validation hindcasting techniques are used to confirm this conclusion. A real runoff forecasting system would, however, require using a real-time precipitation product from an operational data assimilation system. For the Amudarya basin, the correlation between precipitation and subsequent runoff is substantially lower, presumably because of a lower quality of ERA-15 precipitation estimates within the tropical weather system, and/or due to a lower quality of the withdrawal-corrected runoff figures.

Droughts, prolonged heatwaves, heavy rainfall and multiple large-scale floods - recent years have shown that global climate change requires more sustainable and timely water management at all levels. In particular, for the optimized use of water resources for irrigation or hydropower generation, it is essential to know their likely availability in the coming months anywhere in the world. This sub-seasonal to seasonal time range is covered by seasonal forecasting systems such as SEAS5 developed by the European Center for Medium-Range Weather Forecasts (ECMWF). These systems have the potential to provide important data for improving water management practices. However, without a bias correction, the data deviate strongly from the actual data. We have shown for several regions of the world that the Bias Correction and Spatial Disaggregation (BCSD) method can significantly improve predictive capability. By storing fixed cumulative distribution functions (CDFs) and parallelization, we were able to extend the system from the regional to the global level, i.e. to produce BCSD predictions for the entire globe and present this version at EGU24.Our next step is to evaluate the resulting seasonal forecasts in terms of their predictive quality. This evaluation is carried out using several measures of quality, including the Continuous Ranked Probability Skill Score (CRPSS) and the Brier Skill Score (BSS). To illustrate this, the strengths and weaknesses of the bias-corrected seasonal forecasts are highlighted using two regions. The focus is on the Sahel region, which has a lower forecast quality despite its high social relevance, and on the Lake Victoria catchment area, for which a high forecast quality is achieved. The aim is to achieve as precise an assessment as possible of the global forecast quality, which allows a realistic assessment of the forecasts, particularly in regions with strong fluctuations in water availability. By providing this bias-corrected forecast data in near real time together with an analysis of its quality, better estimates will be available for direct use by water managers or as input for subsequent modeling processes.

In recent years, alternating drought and extreme precipitation events have highlighted the need for subseasonal to seasonal forecasts of the terrestrial water cycle. In particular, predictions of the impacts of dry and wet extremes on subsurface water resources are crucial to provide stakeholders in agriculture, forestry, the water sector, and other fields with information supporting the sustainable use of these resources.In this context, we release an experimental Water Resources Bulletin (https://adapter-projekt.de/bulletin/index.html) four times per year, offering probabilistic forecasts of the total subsurface water storage (TSS) anomaly at a 0.6 km resolution, from the surface down to 60 m depth, for the upcoming seven months across Germany. These seasonal forecasts are generated using the integrated, physics-based hydrological model ParFlow/CLM, forced by 50 ensemble members of the SEAS5 seasonal forecast from the European Centre for Medium-Range Weather Forecasts (ECMWF).To evaluate our forecasts, we evaluated six 7-months probabilistic forecasts covering the vegetation period (March to September) for the years 2018 to 2023 with a reference long-term historical time series based on the same ParFlow/CLM setup. The forecast skill was assessed by comparing these seasonal forecasts to a climatology-based 10-member pseudo-forecast over the 2013–2023 period (using the leave-one-out method), extracted from the reference time series.The monthly Continuous Ranked Probability Skill Score (CRPSS), which evaluates the ensemble distribution based on daily TSS data, indicates that the probabilistic forecast outperforms the climatology-based pseudo-forecast in most regions, except in 2018 and, to a lesser extent, in 2020 and 2022. This can be attributed to an under-representation of extremely dry members in the ensemble, combined with the memory effect of the initial conditions at increasing soil depths. For example, while March 2018 started with a slightly above-average TSS and experienced a strong meteorological drought leading to an agricultural drought, the initial TSS anomaly in March 2019 was already negative, with a less pronounced precipitation deficit during the vegetation period. This resulted in a much higher forecast skill, because of the memory effect accurately simulated with the physics-based model. Notably, the forecast skill only slightly decreases with increasing lead time, both for precipitation and TSS.The analysis of the Relative Operating Characteristic Skill Score (ROCSS) for the lower quintile of the TSS distribution assesses whether negative TSS anomalies (i.e., droughts) are adequately represented within the probabilistic forecast ensemble. The results are consistent with those of the CRPSS, showing lower skill in 2018. Nevertheless, the ROCSS analysis overall indicates moderate to high skill for the probabilistic forecast, while the climatology-based pseudo-forecast demonstrates no skill. This confirms that the dry conditions experienced in central Europe in recent years were captured within the probabilistic forecast, underlining the added value of these forecasts and their usefulness in the experimental Water Resources Bulletin.

Abstract. Global seasonal forecasts of meteorological drought using the standardized precipitation index (SPI) are produced using two data sets as initial conditions: the Global Precipitation Climatology Centre (GPCC) and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis (ERAI); and two seasonal forecasts of precipitation, the most recent ECMWF seasonal forecast system and climatologically based ensemble forecasts. The forecast evaluation focuses on the periods where precipitation deficits are likely to have higher drought impacts, and the results were summarized over different regions in the world. The verification of the forecasts with lead time indicated that generally for all regions the least reduction on skill was found for (i) long lead times using ERAI or GPCC for monitoring and (ii) short lead times using ECMWF or climatological seasonal forecasts. The memory effect of initial conditions was found to be 1 month of lead time for the SPI-3, 4 months for the SPI-6 and 6 (or more) months for the SPI-12. Results show that dynamical forecasts of precipitation provide added value with skills at least equal to and often above that of climatological forecasts. Furthermore, it is very difficult to improve on the use of climatological forecasts for long lead times. Our results also support recent questions of whether seasonal forecasting of global drought onset was essentially a stochastic forecasting problem. Results are presented regionally and globally, and our results point to several regions in the world where drought onset forecasting is feasible and skilful.

Initialization of land surface prognostic variables is a crucial issue for short- and medium-range forecasting as well as at seasonal timescales. In this study, two sequential soil moisture analysis schemes are tested, both based on the comparison between observed and predicted 2-m parameters: the nudging technique used operationally at the European Centre for Medium-Range Weather Forecasts (ECMWF) and the optimum interpolation technique proposed by J. F. Mahfouf and used operationally at Meteo-France. Both techniques compute the soil moisture increments as a linear function of analysis increments of 2-m parameters (specific humidity at ECMWF, temperature and relative humidity at Meteo-France). Following the preliminary study by Y. Hu et al., the optimum interpolation technique has been adapted to the four soil-level ECMWF land surface scheme. Both methods are tested in the ECMWF single column model, which has been run for 4 months in 1987 at a grid point close to the location of the First Intern...

This report is the summary of our studies on seasonal weather forecasting made during this year. (1) Empirical rules concerning seasonal weather forecasting were collected and arranged, several conclusions on mode of seasonal weather change deduced. (2) Anomaly of seasonal weather was researched and average which is most convenient for seasonal weather forecasting are derived. (3) Method of periodegram analysis was discussed and 5-day mean value of temperature was analysed.24 days',34 days' and 44 days' periodicity were found to be stable. (4) A method of extrapolation by such the periodicity was proposed which is applicable to the seasonal weather forecasting. (5) Mechanism of 34 days' periodicity was suggested, but we need further research to confirm the suggestion (6) The contents are following:1. Preface2. Brief history of the studies in seasonal forecasting in Japan3. Weather proverbs on seasonal weather forecasting4. Collection of correlation coefficients5. Seasonal correlation6. Meaning of seasonal forecasting7. Basic change and perturbation8. Statistical characters of perturbation9. Time interval of the average in seasonal weather changes10. Secular change of the normal values11. Periodicity in seasonal weather changes12. Periodograin analysis of 5-day mean values of temperature and pressure13. Extrapolation by the periodicity15.25.5 days' and 34 day' periodicities.

This paper presents the Soil Moisture and Ocean Salinity (SMOS) mission data assimilation activities conducted at the European Centre for Medium-Range Weather Forecasts (ECMWF) to analyse soil moisture for Numerical Weather Prediction (NWP) applications. Two different approaches are presented based on SMOS brightness temperature and SMOS neural network soil moisture data assimilation, respectively. For the first approach, SMOS brightness temperature data assimilation relies on forward modelling. Long term results, spanning the SMOS period, of SMOS forward modelling, monitoring and data assimilation are presented. They emphasize the relevance of SMOS data for monitoring and to support NWP model developments. For the second approach, a SMOS soil moisture product has been produced based on a Neural Network (NN) trained on ECMWF soil moisture. So, the SMOS-ECMWF NN soil moisture product captures the SMOS signal variability in time and space, while by design its climatology is consistent with that of the ECMWF soil moisture, which makes it suitable for data assimilation purpose. This approach, initially tested for 2012 in a global scale stand alone approach, shows that SMOS NN data assimilation slightly improves the two-metre air temperature forecast in the short range at regional scale. For NWP applications this approach has been further developed with a near real time production of the SMOS-ECMWF NN soil moisture product, with the implementation of the SMOS NN data assimilation in the ECMWF Integrated Forecasting System (IFS), and with high resolution (9km) global scale testing compatible with the current ECMWF NWP system.

This article presents the “screen-level and surface analysis only” (SSA) system at the European Centre for Medium-Range Weather Forecasts (ECMWF). SSA is a simplification of the operational land–atmosphere weakly coupled data assimilation (WCDA). The goal of SSA is to provide 1) efficient research into land surface developments in NWP and 2) land reanalyses with land–atmosphere coupling. SSA maintains a coupled forecast model between assimilation cycles, but the atmospheric analysis is not performed; rather, it is forced from an archived analysis. Hence, SSA is much faster than WCDA, although it lacks feedback between the land and atmospheric analyses. A global sensitivity analysis was performed over one year to compare the WCDA and SSA systems. Prescribed proxy 2-m temperature/humidity screen-level observation errors were approximately doubled in the soil moisture data assimilation, thereby reducing the average size of the root-zone soil moisture analysis increments by about 60%. The systematic impact of these changes on the WCDA surface and near-surface atmospheric dynamics was effectively captured by SSA, although the short-term impact was underestimated. Importantly, the SSA forecast verification scores accurately reflected those of WCDA: atmospheric 1–10-day temperature/humidity forecasts were degraded in the tropics and lower midlatitudes up to about 700 hPa. The soil moisture analysis performance was not significantly impacted. These results endorse SSA as an NWP research tool and confirm the role of assimilating proxy screen-level observations in the soil moisture analysis to improve weather forecasts. Appropriate use and limitations of SSA are considered.