1. 66-SE-CMEMS-CALL2: Lot-3
BRONCO: Benefits of dynamically modelled river discharge input for ocean and coupled atmosphere-land-ocean systems
Hao Zuo (PI), Eric de Boisséson, Ervin Zsoter, Shaun Harrigan, Patricia de Rosnay, Fredrik Wetterhall
Acknowledgement
Julien Paul from Mercator and Sebastien Theeten from IFREMER for helping with implementation of river runoff in NEMO 

2. Mean SSS bias in ORAS5 (Zuo et al., 2019)
Daily SSS in ORAS5 (2018 July-Sep)

3. CMEMS SE Mid-Term Meeting, Toulouse, France, March 18-22 2019
BRONCO
This study is in line with the R&D priority in CMEMS service evolution to provide continuous enhancement of forcing techniques at lateral boundaries (coasts, open boundaries).
The main objective targets for R&D area 6 for seamless interactions between CMEMS and coastal systems, and two priority topics in CMEMS Call 66: Lot-3
Improved and standardised inputs of freshwater flows (and associated river inputs of particulate and dissolved matter) and homogenised river forcing approaches in global, regional and coastal models;
Improve the interfaces/interactions between coastal monitoring and modelling systems and CMEMS.
This study is also consistent with the R&D area 8 for ocean climate products, indicators and scenarios, by providing improvement in physical consistency and realism in ocean reanalyses, which can be used to infer possible changes in the ocean state at regional and coastal levels.
CMEMS SE Mid-Term Meeting, Toulouse, France, March

4. CMEMS SE Mid-Term Meeting, Toulouse, France, March 18-22 2019
Interests
River freshwater input is crucial in modelling global ocean, i.e. coastal and off-shore SSS, near-surface mixing, freshwater transport, sea-level changes, global climate …
Most current CMEMS services rely on river discharge data with various deficiencies
Overly simplistic runoff data: Climatology with no inter-annual variation
Inconsistent forcing: between atmospheric and hydrologic, and between different services
Development of an improved, consistent and standardised river freshwater input is of interest to all CMEMS services.
CMEMS SE Mid-Term Meeting, Toulouse, France, March

5. Objectives The objective of this project is
To develop an improved, calibrated and standardised freshwater input to ocean models from river discharge reanalysis driven by atmospheric forcing consistent to the ocean model, capitalizing on recent developments from the ERA5 (C3S) and GloFAS (EMS).
To assess and report the quantified benefits of using a river discharge reanalysis over climatology as input into global ocean reanalysis system ORAS5 (GLO-RAN).
Such product can provide coastal freshwater boundary conditions for ocean only or coupled atmosphere-land-ocean models, with the potential to enhance the quality of the CMEMS services in the coastal, regional and global scales.

6. How to achieve
Production and evaluation of GloFAS historical river discharge reanalysis under ERA5 forcing (WP1.1)
Implementation of GloFAS river discharge forcing in the NEMO ocean model (WP1.2)
Design/run impact study experiments using the ECMWF ocean and sea-ice reanalysis system and GloFAS-based runoff forcing (WP2.1)
Assessment of benefits from using the GloFAS historical river discharge over a climatology in a global configuration ECMWF ocean synthesis (WP2.2)

7. Main contributors Hao Zuo (PI), ECMWF Fredrik Wetterhall, ECMWF
Patricia de Rosnay, ECMWF
Ervin Zsoter, ECMWF
Eric de Boisséson, ECMWF

8. Summary of achievements
Specific Objectives
Status
Scientific results
Potential impact on CMEMS
Calibration of GloFAS river discharge
Done
The GloFAS simulation skill has improved after calibration of LISFLOOD parameters (Hirpa et al., 2018)
Improve GloFAS reanalysis product quality
Production and evaluation of GloFAS river discharge reanalysis under ERA5 forcing
On-going
Production of GloFAS historical reanalysis using official ERA5 forcing finished for ; Preliminary evaluation shows good overall performance
Provide a standardized and calibrated historical land freshwater input for the ocean model
Implementation of GloFAS river discharge forcing in the NEMO ocean model
A new method has been developed to convert GloFAS reanalysis to NEMO runoff forcing
Provide a testing coastal runoff data set useable for the NEMO ocean model
Design/run impact study experiments with GloFAS-based runoff forcing
Sensitivity experiments carried out for assessing GloFAS discharge data with respect to BT06 climatology
Proof of concept study using ocean synthesis driven by GloFAS river runoff
Evaluation of GloFAS runoff forcing with ocean reanalysis
To do
Provide assessment and report on quantified benefits of using GloFAS runoff in the ocean model

9. Emergency Management Service
Overview of the GloFAS
Land surface model
River routing model
ERA5
GloFAS system
The Copernicus EMS Global Flood Awareness System (GloFAS, is an operational probabilistic flood forecasting systems (Alfieri et al., 2013).
GloFAS upgraded to version 2.0 in Nov 2018, with a major calibration exercise of the hydrological routing parameters in Listflood (Hirpa et. al., 2018). This results in improved discharge simulation skill for the majority of 1287 stations.

10. GloFAS v2 performance gain
(After calibration using 1287 station streamflow observations ( ))
𝐾𝐺𝐸 𝑠𝑠 = change of skill score
Improved performance: 𝐾𝐺𝐸 𝑠𝑠 > Deteriorated performance: 𝐾𝐺𝐸 𝑠𝑠 < 0
The best 𝐾𝐺𝐸 𝑠𝑠 = 1 (perfect simulation)
The calibration used ECMWF re-forecast forcing (surface and sub-surface runoff) from June 1995 to June 2015, and was conducted on 1287 stations worldwide with available daily discharge observations. The calibration was performed using an evolutionary optimization algorithm with the Kling-Gupta Efficiency (KGE, Gupta et al., 2009) as objective function. As a result of the calibration, the version 2.0 discharge simulation skill has improved for the majority of stationswhere: KGESS denotes KGE skill score; KGEdef is KGE with defaultfound skill loss for 10% during the validation period. The median KGEparameters; KGEcal is KGE with calibrated parameters; and KGEperf in-skill score is 0.12 (P90=0.42 and P10=−0.075). In a similar patterndicate KGE of a perfect simulation (=1). Positive (negative)to the calibration period, the skill score map shows that the majority ofKGESSvalues indicate improved (deteriorated) skill after calibration.the skill loss occurred in North America.The best KGESS value is 1. For each case, the KGE was computed with reference to streamflow observations (Qobs). A similar skill score com Regional breakdownputation was repeated for R and NSE. In order to have a comparableTo investigate the regional variations and the impact of catchmentrange of skill scores (i.e., with the best score being 1), the B skill scorearea on the skill gain we present the regional breakdown of the KGE,(BSS) was computed using the absolute values of the percent bias asNSE and R skill scores (Fig. 5). Results reveal that the skill score variesfollows:

11. GloFAS discharge (m3/s)
Dai et al., 2009
GloFAS discharge (m3/s)
ESAS pre-SAC 2018

12. GloFAS historical river discharge reanalysis
Consistent/high quality atmospheric forcing: ERA5 climate reanalysis (C3S product)
Unprecedentedly temporal/spatial resolution: 0.1 degree and daily maps
Long cover period: (potential backward extension, depends on ERA5)
Easy to use format: Netcdf with NEMO compatible test data set
Potential to go RT
Dai et al., 2009
Forcing from Qian et al., 2006
Monthly and only at the farthest downstream gauge stations
Cover period: , no recent data

13. ORAS5 system
ORAS5 is the 5th generation of ECMWF ocean and sea-ice ensemble reanalysis system (Zuo et al., 2018, 2019). Supported by CMEMS GLO MFC, ORAS5 forms part of the CMEMS ocean ensemble physical reanalysis product (GLOBAL_REANALYSIS_PHY_001_026) and provide data for the CMEMS OMI services.
The ORAS5 system
Ocean: NEMOv3.4
Sea-ice: LIM2
Resolution: ¼ degree with 75 levels
Assimilation: 3DVAR-FGAT
Forcing: ERA5
River Runoff
Zuo et al., 2019
Overview of the ORAS5 setup

14. Sensitivity in ocean simulation
Name
DT02 river runoff
River0
No input
River50
50%
River100
100%
River200
200%
Model simulated ocean state is sensitive to variation of land freshwater input. A series of ocean simulation have been carried out with the ECMWF ORAS5 system (without DA), using river runoff climatology from Dai and Trenberth (2002).
Ensemble spread of SSS
Ens spread of max AMOC

15. Work progress
WP1.1 Production and Evaluation of GloFAS historical river discharge
Production of GloFAS global river discharge reanalysis under official ERA5 forcing has finished for period
Preliminary evaluation results suggest that the overall performance of GloFAS discharge reanalysis is reasonably good in general when verified against a global network of discharge observations
Evaluation can not be carried out in, e.g. Maritime Continent due to missing of obs.
A climatology of GloFAS coast discharge data set has been prepared for development of implementation method in the NEMO ocean model

16. WP1.1 Production and Evaluation of GloFAS historical river discharge
Performance of the discharge reanalysis was assessed against observations using the modified Kling-Gupta Efficiency metric ( 𝑲𝑮𝑬 𝒎𝒐𝒅 : Gupta et al., 2009; Kling et al., 2012). It can be decomposed into three components that are important for assessing hydrological dynamics: temporal errors through correlation (𝒓), bias errors (𝜷), and variability errors (𝜸). 𝐾𝐺𝐸 𝑚𝑜𝑑 is given by:
𝐾𝐺𝐸 𝑚𝑜𝑑 =1− (𝑟−1) 2 + (𝛽−1) 2 + (𝛾−1) 2
𝛽= 𝜇 𝑠 𝜇 𝑜
𝛾= 𝜎 𝑠 𝜇 𝑠 𝜎 𝑜 𝜇 𝑜
𝜇 is the mean and 𝜎 the standard deviation
indices 𝑠 and 𝑜 represent simulated and observed discharge
𝐾𝐺𝐸 𝑚𝑜𝑑 = 1 (perfect simulation)
< 0 (poor simulation)

17. WP1.1 Production and Evaluation of GloFAS historical river discharge
𝐾𝐺𝐸 𝑚𝑜𝑑 = 1 (perfect simulation)
< 0 (poor simulation)
Better simulation
Performance is best in Brazil (particularly the Amazon basin), central Europe, and eastern and western regions of the US. Performance is poor (i.e. 𝐾𝐺𝐸 𝑚𝑜𝑑 < 0) in many catchments in Africa, the North American Great Plains extending into Mexico, with notable patches in north eastern Brazil, Thailand, and Spain.
Modified Kling-Gupta Efficiency ( 𝐾𝐺𝐸 𝑚𝑜𝑑 ) scores for the GloFAS discharge reanalysis against discharge observations ( , within 1907 catchments > 500 km^2)

18. Decomposition of 𝑲𝑮𝑬 𝒎𝒐𝒅
(a) Pearson correlation
(b) bias ratio
(c) variability ratio
Small mean
Best
Large mean
Performance is best in Brazil (particularly the Amazon basin), central Europe, and eastern and western regions of the US. Performance is poor in many catchments in Africa, the North American Great Plains extending into Mexico, with notable patches in north eastern Brazil, Thailand, and Spain.
Large positive bias errors (bias ratio > 1), in particular, are contributing to poor performance of the discharge reanalysis.
The vast majority of catchments (98 %) show positive correlation (Figure 2a) with a global median Pearson correlation coefficient of 0.6. Figure 2b shows that discharge reanalysis is negatively biased in 64 % of catchments (i.e. bias ratio < 1) with global median bias (as percentage) of -17 %. Figure 2c shows the variability of reanalysis time-series is lower than observations in 59 % of catchments (i.e. variability ratio < 1) but is less severe than bias errors with global median values (as percentage) of -8 %. The decomposition of the 𝐾𝐺𝐸 𝑚𝑜𝑑 is useful for attributing which components are contributing to good and poor GloFAS discharge reanalysis performance.
Best
Large var.
Small var.

19. Work progress
WP1.2 Implementation of GloFAS river discharge into the NEMO ocean model
Similar approach as described in Bourdallé-Badie and Treguier (2006, BT06)
Identify major rivers (> 20km^3/yr) and sorted them by order of annual discharge magnitude
Coordinate mapping from river coastal discharge to NEMO ocean model
Create river mask for spreading freshwater near the river mouths
Add Antarctic coastal runoff from Jacobs et al (1992)
A GloFAS climatology coast runoff forcing for NEMO has been produced for impact study
Revise implementation method following outcome from WP2.1

20. Flow chart of implementation steps
WP1.2 Implementation of GloFAS river discharge into the NEMO ocean model
Flow chart of implementation steps
Revise method based on model performance
Raw GLOFAS map
Map discharge points to ORCA025 coastline (<100km)
Identify and create major river mask with CheckBMG
Spread major river discharges on the river mask
Add minor river discharges
Add Antarctic runoff
Total GLOFAS-based coastal runoff on NEMO ORCA025
Test in NEMO ocean model

21. Create of river discharge spread mask near the river mouths
WP1.2 Implementation of GloFAS river discharge into the NEMO ocean model
Create of river discharge spread mask near the river mouths
Amazon
Mississippi
Ganges
black squares = BT06
red dots = GLOFAS

22. Climatology of GloFAS river runoff forcing in NEMO ocean model
Bourdalle and Treguier, 2006 (BT06), derived using runoff data from Dai and Trenberth, 2002
Amazon
Global (exclude Antarctic)
Maritime Continent
The total discharge (excluding Antarctic) in GloFAS climatology is 1.39 Sv, about 0.16 Sv (13%) more than the BT06 runoff. The GloFAS runoff in MC region is higher (~0.12 Sv) than BT06, which accounts for most difference in global annual discharge. The Amazon river in GloFAS is slightly lower than BT06 (which also includes Tapajos and Xingu, total equivalent to ~0.022 Sv). and with increased seasonal variation.

23. Work progress
WP2.1 Design/run impact study experiments using different runoff forcings
Sensitivity experiments have been carried out with either GloFAS climatology runoff forcing or BT06 forcing
Repeat sensitivity experiments under both ERA-int and ERA5 forcing, and for different periods.
Evaluation of ECVs (SST, SSS, Sea-level, SIT) have been carried out for all experiments
Such an evaluation provides important feedback and help improving the processing method when creating NEMO compatible forcing from GloFAS data

24. Summary of sensitivity experiments
(NEMO+LIM2, ORCA025.L75 config, no DA)
Exps
Note
Forcing
runoff
period
Shot name
h3l9
CTL with ERA-I and BT06 climatology
ERA-int
Bourdalle and Treguier, 2006
h3kd
CTL with ERA-I and GloFAS climatology v1
GloFAS climatology (ver1)
h3vm
REF
h408
CTL with ERA-I and GloFAS climatology v2
GloFAS climatology (ver2)
GloV2
h4qx
CTL with GloFAS climatology v2 revised
GloFAS clim ver2, revised
GloV2.1 (50% MC river runoff)
h3kh
CTL with BT06 climatology
ERA5 hourly
h3kk
CTL with GloFAS climatology v1
h3vj
CTL with GloFAS climatology v2
CMEMS SE Mid-Term Meeting, Toulouse, France, March

25. CMEMS SE Mid-Term Meeting, Toulouse, France, March 18-22 2019
WP2.1 Design/run impact study experiments using different runoff forcings
Mean Sea Surface Salinity differences
GloV2 – REF
GloV2.1 – REF
CMEMS SE Mid-Term Meeting, Toulouse, France, March

26. CMEMS SE Mid-Term Meeting, Toulouse, France, March 18-22 2019
WP2.1 Design/run impact study experiments using different runoff forcings
RMSE differences: against EN4 in-situ obs,
GloV2 - REF
GloV2.1 - REF
degradation
improvement
CMEMS SE Mid-Term Meeting, Toulouse, France, March

27. CMEMS SE Mid-Term Meeting, Toulouse, France, March 18-22 2019
WP2.1 Design/run impact study experiments using different runoff forcings
RMSE differences: against SMOS L3 de-biased obs,
GloV2 - REF
GloV2.1 - REF
degradation
improvement
degradation
improvement
CMEMS SE Mid-Term Meeting, Toulouse, France, March

28. Uncertainty in satellite Sea Surface Salinity observations
SMOS SSS ( )
SMAP SSS ( )
CMEMS SE Mid-Term Meeting, Toulouse, France, March

29. CMEMS SE Mid-Term Meeting, Toulouse, France, March 18-22 2019
RMSE differences: Glov2.1 – REF
SST
Sea-level
Temperature 0-200m
Sea-ice concentration
Sea-ice thickness
CMEMS SE Mid-Term Meeting, Toulouse, France, March

30. CMEMS SE Mid-Term Meeting, Toulouse, France, March 18-22 2019
WP2.1 Design/run impact study experiments using different runoff forcings
AMOC volume transport at 26.5N
CMEMS SE Mid-Term Meeting, Toulouse, France, March

31. To do
WP2.2 Assessment of the impact of GloFAS runoff forcing using ORA
Run ORAS5 equivalent experiment with the full GloFAS reanalysis discharge for two different period : and
Assessment on both global and regional scales, against independent observation data sets for a series of ECVs

32. CMEMS SE Mid-Term Meeting, Toulouse, France, March 18-22 2019
Issues and risks
Performance of model simulated ocean state is subject to
Consistency/balance between different forcings: ERA5 has improved precipitation in the tropics compared to ERA-int, but may be still overestimated.
Quality of input river discharge data set : GloFAS has relatively poor performance in Africa, central America, north eastern Brazil and Thailand
Method of implementation under NEMO model: sub-optimal in river mask definition and ocean model parameter settings (e.g. enhanced mixing depth at river mouth)
Quality of verification data set (try SMAP SSS)
Production of GloFAS historical discharge reanalysis has been seriously affected due to the delay in ERA5 release
CMEMS SE Mid-Term Meeting, Toulouse, France, March

33. Following works
Continue evaluation of GloFAS historical reanalysis with new available observation and for different period
Continue optimizing the method of implementing GloFAS forcing in the NEMO ocean model
Produce monthly mean GloFAS coastal discharge between to force NEMO ocean model in ORCA025.L75 configuration.
Continue impact study within the ECMWF Ocean ReAnalysis (ORA) system using the full GloFAS monthly reanalysis from
Assessment of the new GloFAS-based runoff forcing in a context of global ocean reanalysis

34. Outcomes
A global river discharge reanalysis data set from the GloFAS system from 1979 to 2017, driven by ERA5 forcing
A NEMO compatible river runoff test data set derived from GloFAS reanalysis
Conducting impact study experiments within the ECMWF Ocean ReAnalysis (ORA) system
Assessment of the new GloFAS-based runoff forcing in global ocean and coastal areas, on ocean Essential Climate Variables' (ECVs) simulations.

35. Potential impacts for CMEMS
A improved and reliable river discharge data set for CMEMS services
CEMS GloFAS river-discharge reanalysis driven by ERA5 forcing
Unprecedented temporal and spatial resolution with realistic variability
Consistent atmospheric boundary conditions in both the hydrology and ocean components
Homogenised river forcing approaches in global, regional and coastal model
Lesson learned from product assessment will be passed on to CMEMS
Improve interaction between coastal monitoring and modelling system and CMEMS by removing the inconsistency in land freshwater inputs seen by different systems
Building synergies between C3S, CEMS and CMEMS

36. Deliverables Quarterly report (every 3 months) Up-to-date
Mid-term report (K+12) Submitted
Final report (K+23) To do
GloFAS global river discharge reanalysis data set (K+23) On-going
The proposed GloFAS river discharge reanalysis data set will be made easily available to the user community, and could be disseminated through the C3S Climate Data Store. The NEMO compatible GloFAS-based run-off data can be made available but only for NEMO configuration used by ECMWF.

37. Use of resources Name Man/month Hao Zuo 3 Ervin Zsoter
Eric de Boisseson 2
Patricia de Rosnay
0.25
Fredrik Wetterhall
With extra helps from the ECMWF GloFAS team (Shaun Harrigan and others)

38. Thank you !