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Emanuel Dutra
,
Frederico Johannsen
, and
Linus Magnusson

Abstract

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 the 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 the fifth-generation ECMWF atmospheric reanalysis (ERA5). 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.

Open access
Rene Orth
,
Emanuel Dutra
, and
Florian Pappenberger

Abstract

The land surface forms an important component of Earth system models and interacts nonlinearly with other parts such as ocean and atmosphere. To capture the complex and heterogeneous hydrology of the land surface, land surface models include a large number of parameters impacting the coupling to other components of the Earth system model.

Focusing on ECMWF’s land surface model Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL), the authors present in this study a comprehensive parameter sensitivity evaluation using multiple observational datasets in Europe. The authors select six poorly constrained effective parameters (surface runoff effective depth, skin conductivity, minimum stomatal resistance, maximum interception, soil moisture stress function shape, and total soil depth) and explore their sensitivity to model outputs such as soil moisture, evapotranspiration, and runoff using uncoupled simulations and coupled seasonal forecasts. Additionally, the authors investigate the possibility to construct ensembles from the multiple land surface parameters.

In the uncoupled runs the authors find that minimum stomatal resistance and total soil depth have the most influence on model performance. Forecast skill scores are moreover sensitive to the same parameters as HTESSEL performance in the uncoupled analysis. The authors demonstrate the robustness of these findings by comparing multiple best-performing parameter sets and multiple randomly chosen parameter sets. The authors find better temperature and precipitation forecast skill with the best-performing parameter perturbations demonstrating representativeness of model performance across uncoupled (and hence less computationally demanding) and coupled settings.

Finally, the authors construct ensemble forecasts from ensemble members derived with different best-performing parameterizations of HTESSEL. This incorporation of parameter uncertainty in the ensemble generation yields an increase in forecast skill, even beyond the skill of the default system.

Full access
Sungmin O
,
Emanuel Dutra
, and
Rene Orth

Abstract

Future climate projections require Earth system models to simulate conditions outside their calibration range. It is therefore crucial to understand the applicability of such models and their modules under transient conditions. This study assesses the robustness of different types of models in terms of rainfall–runoff modeling under changing conditions. In particular, two process-based models and one data-driven model are considered: 1) the physically based land surface model of the European Centre for Medium-Range Weather Forecasts, 2) the conceptual Simple Water Balance Model, and 3) the Long Short-Term Memory-Based Runoff model. Using streamflow data from 161 catchments across Europe, a differential split-sample test is performed, i.e., models are calibrated within a reference period (e.g., wet years) and then evaluated during a climatically contrasting period (e.g., drier years). Models show overall performance loss, which generally increases the more conditions deviate from the reference climate. Further analysis reveals that the models have difficulties in capturing temporal shifts in the hydroclimate of the catchments, e.g., between energy- and water-limited conditions. Overall, relatively high robustness is demonstrated by the physically based model. This suggests that improvements of physics-based parameterizations can be a promising avenue toward reliable climate change simulations. Further, our study illustrates that comparison across process-based and data-driven models is challenging due to their different nature. While we find rather low robustness of the data-driven model in our particular split-sample setup, this must not apply generally; by contrast, such model schemes have great potential as they can learn diverse conditions from observed spatial and temporal variability both at the same time to yield robust performance.

Open access
Emanuel Dutra
,
Pedro Viterbo
,
Pedro M. A. Miranda
, and
Gianpaolo Balsamo

Abstract

Three different complexity snow schemes implemented in the ECMWF land surface scheme Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) are evaluated within the EC-EARTH climate model. The snow schemes are (i) the original HTESSEL single-bulk-layer snow scheme, (ii) a new snow scheme in operations at ECMWF since September 2009, and (iii) a multilayer version of the previous. In offline site simulations, the multilayer scheme outperforms the single-layer schemes in deep snowpack conditions through its ability to simulate sporadic melting events thanks to the lower thermal inertial of the uppermost layer. Coupled atmosphere–land/snow simulations performed by the EC-EARTH climate model are validated against remote sensed snow cover and surface albedo. The original snow scheme has a systematic early melting linked to an underestimation of surface albedo during spring that was partially reduced with the new snow schemes. A key process to improve the realism of the near-surface atmospheric temperature and at the same time the soil freezing is the thermal insulation of the snowpack (tightly coupled with the accuracy of snow mass and density simulations). The multilayer snow scheme outperforms the single-layer schemes in open deep snowpack (such as prairies or tundra in northern latitudes) and is instead comparable in shallow snowpack conditions. However, the representation of orography in current climate models implies limitations for accurately simulating the snowpack, particularly over complex terrain regions such as the Rockies and the Himalayas.

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Emanuel Dutra
,
Gianpaolo Balsamo
,
Pedro Viterbo
,
Pedro M. A. Miranda
,
Anton Beljaars
,
Christoph Schär
, and
Kelly Elder

Abstract

A new snow scheme for the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface model has been tested and validated. The scheme includes a new parameterization of snow density, incorporating a liquid water reservoir, and revised formulations for the subgrid snow cover fraction and snow albedo. Offline validation (covering a wide range of spatial and temporal scales) includes simulations for several observation sites from the Snow Models Intercomparison Project-2 (SnowMIP2) and global simulations driven by the meteorological forcing from the Global Soil Wetness Project-2 (GSWP2) and by ECMWF Re-Analysis ERA-Interim. The new scheme reduces the end of season ablation biases from 10 to 2 days in open areas and from 21 to 13 days in forest areas. Global GSWP2 results are compared against basin-scale runoff and terrestrial water storage. The new snow density parameterization increases the snow thermal insulation, reducing soil freezing and leading to an improved hydrological cycle. Simulated snow cover fraction is compared against NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) with a reduction of the negative bias of snow-covered area of the original snow scheme. The original snow scheme had a systematic negative bias in surface albedo when compared against Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data. The new scheme reduces the albedo bias, consequently reducing the spatial- and time-averaged surface net shortwave radiation bias by 5.2 W m−2 in 14% of the Northern Hemisphere land. The new snow scheme described in this paper was introduced in the ECMWF operational forecast system in September 2009 (cycle 35R3).

Full access
Ervin Zsótér
,
Florian Pappenberger
,
Paul Smith
,
Rebecca Elizabeth Emerton
,
Emanuel Dutra
,
Fredrik Wetterhall
,
David Richardson
,
Konrad Bogner
, and
Gianpaolo Balsamo

Abstract

In the last decade operational probabilistic ensemble flood forecasts have become common in supporting decision-making processes leading to risk reduction. Ensemble forecasts can assess uncertainty, but they are limited to the uncertainty in a specific modeling system. Many of the current operational flood prediction systems use a multimodel approach to better represent the uncertainty arising from insufficient model structure. This study presents a multimodel approach to building a global flood prediction system using multiple atmospheric reanalysis datasets for river initial conditions and multiple TIGGE forcing inputs to the ECMWF land surface model. A sensitivity study is carried out to clarify the effect of using archive ensemble meteorological predictions and uncoupled land surface models. The probabilistic discharge forecasts derived from the different atmospheric models are compared with those from the multimodel combination. The potential for further improving forecast skill by bias correction and Bayesian model averaging is examined. The results show that the impact of the different TIGGE input variables in the HTESSEL/Catchment-Based Macroscale Floodplain model (CaMa-Flood) setup is rather limited other than for precipitation. This provides a sufficient basis for evaluation of the multimodel discharge predictions. The results also highlight that the three applied reanalysis datasets have different error characteristics that allow for large potential gains with a multimodel combination. It is shown that large improvements to the forecast performance for all models can be achieved through appropriate statistical postprocessing (bias and spread correction). A simple multimodel combination generally improves the forecasts, while a more advanced combination using Bayesian model averaging provides further benefits.

Full access
Hylke E. Beck
,
Albert I. J. M. van Dijk
,
Pablo R. Larraondo
,
Tim R. McVicar
,
Ming Pan
,
Emanuel Dutra
, and
Diego G. Miralles

Abstract

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.

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Manuela Grippa
,
Laurent Kergoat
,
Aaron Boone
,
Christophe Peugeot
,
Jérôme Demarty
,
Bernard Cappelaere
,
Laetitia Gal
,
Pierre Hiernaux
,
Eric Mougin
,
Agnès Ducharne
,
Emanuel Dutra
,
Martha Anderson
,
Christopher Hain
, and
ALMIP2 Working Group

Abstract

Land surface processes play an important role in the West African monsoon variability. In addition, the evolution of hydrological systems in this region, and particularly the increase of surface water and runoff coefficients observed since the 1950s, has had a strong impact on water resources and on the occurrence of floods events. This study addresses results from phase 2 of the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project (ALMIP2), carried out to evaluate the capability of different state-of-the-art land surface models to reproduce surface processes at the mesoscale. Evaluation of runoff and water fluxes over the Mali site is carried out through comparison with runoff estimations over endorheic watersheds as well as evapotranspiration (ET) measurements. Three remote-sensing-based ET products [ALEXI, MODIS, and Global Land Evaporation Amsterdam Model (GLEAM)] are also analyzed. It is found that, over deep sandy soils, surface runoff is generally overestimated, but the ALMIP2 multimodel mean reproduces in situ measurements of ET and water stress events rather well. However, ALMIP2 models are generally unable to distinguish among the two contrasted hydrological systems typical of the study area. Employing as input a soil map that explicitly represents shallow soils improves the representation of water fluxes for the models that can account for their representation. Shallow soils are shown to be also quite challenging for remote-sensing-based ET products, even if their effect on evaporative loss was captured by the diagnostic thermal-based ALEXI. A better representation of these soils, in soil databases, model parameterizations, and remote sensing algorithms, is fundamental to improve the estimation of water fluxes in this part of the Sahel.

Full access
Paul A. Dirmeyer
,
Liang Chen
,
Jiexia Wu
,
Chul-Su Shin
,
Bohua Huang
,
Benjamin A. Cash
,
Michael G. Bosilovich
,
Sarith Mahanama
,
Randal D. Koster
,
Joseph A. Santanello
,
Michael B. Ek
,
Gianpaolo Balsamo
,
Emanuel Dutra
, and
David M. Lawrence

Abstract

This study compares four model systems in three configurations (LSM, LSM + GCM, and reanalysis) with global flux tower observations to validate states, surface fluxes, and coupling indices between land and atmosphere. Models clearly underrepresent the feedback of surface fluxes on boundary layer properties (the atmospheric leg of land–atmosphere coupling) and may overrepresent the connection between soil moisture and surface fluxes (the terrestrial leg). Models generally underrepresent spatial and temporal variability relative to observations, which is at least partially an artifact of the differences in spatial scale between model grid boxes and flux tower footprints. All models bias high in near-surface humidity and downward shortwave radiation, struggle to represent precipitation accurately, and show serious problems in reproducing surface albedos. These errors create challenges for models to partition surface energy properly, and errors are traceable through the surface energy and water cycles. The spatial distribution of the amplitude and phase of annual cycles (first harmonic) are generally well reproduced, but the biases in means tend to reflect in these amplitudes. Interannual variability is also a challenge for models to reproduce. Although the models validate better against Bowen-ratio-corrected surface flux observations, which allow for closure of surface energy balances at flux tower sites, it is not clear whether the corrected fluxes are more representative of actual fluxes. The analysis illuminates targets for coupled land–atmosphere model development, as well as the value of long-term globally distributed observational monitoring.

Open access
Augusto C. V. Getirana
,
Emanuel Dutra
,
Matthieu Guimberteau
,
Jonghun Kam
,
Hong-Yi Li
,
Bertrand Decharme
,
Zhengqiu Zhang
,
Agnes Ducharne
,
Aaron Boone
,
Gianpaolo Balsamo
,
Matthew Rodell
,
Ally M. Toure
,
Yongkang Xue
,
Christa D. Peters-Lidard
,
Sujay V. Kumar
,
Kristi Arsenault
,
Guillaume Drapeau
,
L. Ruby Leung
,
Josyane Ronchail
, and
Justin Sheffield

Abstract

Despite recent advances in land surface modeling and remote sensing, estimates of the global water budget are still fairly uncertain. This study aims to evaluate the water budget of the Amazon basin based on several state-of-the-art land surface model (LSM) outputs. Water budget variables (terrestrial water storage TWS, evapotranspiration ET, surface runoff R, and base flow B) are evaluated at the basin scale using both remote sensing and in situ data. Meteorological forcings at a 3-hourly time step and 1° spatial resolution were used to run 14 LSMs. Precipitation datasets that have been rescaled to match monthly Global Precipitation Climatology Project (GPCP) and Global Precipitation Climatology Centre (GPCC) datasets and the daily Hydrologie du Bassin de l’Amazone (HYBAM) dataset were used to perform three experiments. The Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme was forced with R and B and simulated discharges are compared against observations at 165 gauges. Simulated ET and TWS are compared against FLUXNET and MOD16A2 evapotranspiration datasets and Gravity Recovery and Climate Experiment (GRACE) TWS estimates in two subcatchments of main tributaries (Madeira and Negro Rivers). At the basin scale, simulated ET ranges from 2.39 to 3.26 mm day−1 and a low spatial correlation between ET and precipitation indicates that evapotranspiration does not depend on water availability over most of the basin. Results also show that other simulated water budget components vary significantly as a function of both the LSM and precipitation dataset, but simulated TWS generally agrees with GRACE estimates at the basin scale. The best water budget simulations resulted from experiments using HYBAM, mostly explained by a denser rainfall gauge network and the rescaling at a finer temporal scale.

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