Complementing ERA5 and E-OBS20 with high resolution river discharge

2020 ◽  
Author(s):  
Stefan Hagemann ◽  
Tobias Stacke
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Land Surface ◽  
River Discharge ◽  
River Mouth ◽  
Water Model ◽  
Model Intercomparison ◽  
Station Data ◽  
Intercomparison Project ◽  
Subsurface Runoff

<p>The 0.5° resolution of many global observational datasets is not sufficient for the requirements of current state-of-the-art regional climate model (RCM) simulations over Europe. Here, the ERA5 reanalysis of the ECMWF (C3S 2017) and E-OBS data (Cornes et al. 2018) are frequently used as reference datasets when RCM results are evaluated on resolutions higher than 0.5°. In addition, ERA5 data are also commonly used to force regional ocean models. As ERA data do not comprise river discharges, the lateral forcing of freshwater inflow from land is taken from other data sources, such as station data, runoff climatologies, etc. However, these data are not necessarily consistent with the ERA5 forcing over the ocean surface. If such data are derived from station data, they are only available for specific rivers and not spatially homogeneously distributed for all coastal areas. In addition, they might not be representative for the river mouth if the respective station location is too far away from the river mouth, which is often the case.</p><p>In order to allow a consistent forcing of river discharges and evaluation of simulated hydrological fluxes, we extended ERA5 and E-OBS v20.0e with high resolution river discharge. This also allows a consistent assessment of hydrological changes from these two datasets. The discharge was simulated with the recently developed 5 Min. version of the Hydrological discharge (HD) model (Hagemann et al., submitted). Note that for the development of this HD model version, no river specific parameter adjustments were conducted so that the HD model is generally applicable for climate change studies and over ungauged catchments.</p><p>The HD model requires gridded fields of surface and subsurface runoff as input with a daily temporal resolution or higher. As no large-scale observations of these variables exist, they need to be calculated by a land surface scheme or hydrology model using observed or re-analyzed meteorological data. Here, we used the HydroPy global hydrological model, which is the successor of the MPI-HM model (Stacke and Hagemann 2012). The latter has contributed to the WATCH Water Model Intercomparison Project (WaterMIP; Haddeland et al. 2011) and the inter-sectoral impact model intercomparison project (ISIMIP; Warszawski et al. 2014). Note that ERA5 also comprises archived fields of surface and subsurface runoff, but it turned out that its separation of total runoff is not suitable to generate adequate river discharges with the HD model. In our presentation, we evaluate the simulated discharge using various metrics and consider significant discharge trends over Europe.</p><p><strong>References</strong></p><p>C3S (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS)</p><p>Cornes, R., et al. (2018) J. Geophys. Res. Atmos. 123, doi:10.1029/2017JD028200</p><p>Haddeland, I., et al. (2011). J. Hydrometeorol. 12, doi: 10.1175/2011jhm1324.1</p><p>Hagemann, S., T. Stacke and H. Ho-Hagemann, High resolution discharge simulations over Europe and the Baltic Sea catchment. Frontiers in Earth Sci., submitted.</p><p>Stacke, T. and Hagemann, S. (2012). Hydrol. Earth Syst. Sci. 16, doi: 10.5194/hess-16-2915-2012</p><p>Warszawski, L., et al. (2014) Proc. Natl. Acad. Sci. USA 111, doi: 10.1073/pnas.1312330110</p>

scholarly journals Multimodel Estimate of the Global Terrestrial Water Balance: Setup and First Results

2011 ◽  
Vol 12 (5) ◽  
pp. 869-884 ◽  
Author(s):  
Ingjerd Haddeland ◽  
Douglas B. Clark ◽  
Wietse Franssen ◽  
Fulco Ludwig ◽  
Frank Voß ◽  
...  
Keyword(s):  
Water Balance ◽  
Land Surface ◽  
Water Model ◽  
Hydrological Models ◽  
Model Intercomparison ◽  
Land Surface Models ◽  
Surface Models ◽  
Terrestrial Water ◽  
Intercomparison Project ◽  
Energy Balance Approach

Abstract Six land surface models and five global hydrological models participate in a model intercomparison project [Water Model Intercomparison Project (WaterMIP)], which for the first time compares simulation results of these different classes of models in a consistent way. In this paper, the simulation setup is described and aspects of the multimodel global terrestrial water balance are presented. All models were run at 0.5° spatial resolution for the global land areas for a 15-yr period (1985–99) using a newly developed global meteorological dataset. Simulated global terrestrial evapotranspiration, excluding Greenland and Antarctica, ranges from 415 to 586 mm yr−1 (from 60 000 to 85 000 km3 yr−1), and simulated runoff ranges from 290 to 457 mm yr−1 (from 42 000 to 66 000 km3 yr−1). Both the mean and median runoff fractions for the land surface models are lower than those of the global hydrological models, although the range is wider. Significant simulation differences between land surface and global hydrological models are found to be caused by the snow scheme employed. The physically based energy balance approach used by land surface models generally results in lower snow water equivalent values than the conceptual degree-day approach used by global hydrological models. Some differences in simulated runoff and evapotranspiration are explained by model parameterizations, although the processes included and parameterizations used are not distinct to either land surface models or global hydrological models. The results show that differences between models are a major source of uncertainty. Climate change impact studies thus need to use not only multiple climate models but also some other measure of uncertainty (e.g., multiple impact models).

scholarly journals LS3MIP (v1.0) contribution to CMIP6: the Land Surface, Snow and Soil moisture Model Intercomparison Project – aims, setup and expected outcome

2016 ◽  
Vol 9 (8) ◽  
pp. 2809-2832 ◽  
Author(s):  
Bart van den Hurk ◽  
Hyungjun Kim ◽  
Gerhard Krinner ◽  
Sonia I. Seneviratne ◽  
Chris Derksen ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Land Surface ◽  
Surface Radiation ◽  
The Arctic ◽  
Model Intercomparison ◽  
Soil Moisture Model ◽  
Surface Snow ◽  
Systematic Biases ◽  
Intercomparison Project

Abstract. The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) is designed to provide a comprehensive assessment of land surface, snow and soil moisture feedbacks on climate variability and climate change, and to diagnose systematic biases in the land modules of current Earth system models (ESMs). The solid and liquid water stored at the land surface has a large influence on the regional climate, its variability and predictability, including effects on the energy, water and carbon cycles. Notably, snow and soil moisture affect surface radiation and flux partitioning properties, moisture storage and land surface memory. They both strongly affect atmospheric conditions, in particular surface air temperature and precipitation, but also large-scale circulation patterns. However, models show divergent responses and representations of these feedbacks as well as systematic biases in the underlying processes. LS3MIP will provide the means to quantify the associated uncertainties and better constrain climate change projections, which is of particular interest for highly vulnerable regions (densely populated areas, agricultural regions, the Arctic, semi-arid and other sensitive terrestrial ecosystems). The experiments are subdivided in two components, the first addressing systematic land biases in offline mode (“LMIP”, building upon the 3rd phase of Global Soil Wetness Project; GSWP3) and the second addressing land feedbacks attributed to soil moisture and snow in an integrated framework (“LFMIP”, building upon the GLACE-CMIP blueprint).

scholarly journals CAS-LSM Datasets for the CMIP6 Land Surface Snow and Soil Moisture Model Intercomparison Project

2021 ◽  
Author(s):  
Binghao Jia ◽  
Longhuan Wang ◽  
Yan Wang ◽  
Ruichao Li ◽  
Xin Luo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Grid Point ◽  
Global Ocean ◽  
Surface Model ◽  
Past Century ◽  
Model Intercomparison ◽  
Soil Moisture Model ◽  
Surface Snow ◽  
Intercomparison Project

AbstractThe datasets of the five Land-offline Model Intercomparison Project (LMIP) experiments using the Chinese Academy of Sciences Land Surface Model (CAS-LSM) of CAS Flexible Global-Ocean-Atmosphere-Land System Model Grid-point version 3 (CAS FGOALS-g3) are presented in this study. These experiments were forced by five global meteorological forcing datasets, which contributed to the framework of the Land Surface Snow and Soil Moisture Model Intercomparison Project (LS3MIP) of CMIP6. These datasets have been released on the Earth System Grid Federation node. In this paper, the basic descriptions of the CAS-LSM and the five LMIP experiments are shown. The performance of the soil moisture, snow, and land-atmosphere energy fluxes was preliminarily validated using satellite-based observations. Results show that their mean states, spatial patterns, and seasonal variations can be reproduced well by the five LMIP simulations. It suggests that these datasets can be used to investigate the evolutionary mechanisms of the global water and energy cycles during the past century.

scholarly journals The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6

2016 ◽  
Vol 9 (9) ◽  
pp. 3461-3482 ◽  
Author(s):  
Brian C. O'Neill ◽  
Claudia Tebaldi ◽  
Detlef P. van Vuuren ◽  
Veronika Eyring ◽  
Pierre Friedlingstein ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
21St Century ◽  
Integrated Assessment ◽  
Climate Model ◽  
Model Intercomparison ◽  
Wide Range ◽  
Scenario Model ◽  
Intercomparison Project

Abstract. Projections of future climate change play a fundamental role in improving understanding of the climate system as well as characterizing societal risks and response options. The Scenario Model Intercomparison Project (ScenarioMIP) is the primary activity within Phase 6 of the Coupled Model Intercomparison Project (CMIP6) that will provide multi-model climate projections based on alternative scenarios of future emissions and land use changes produced with integrated assessment models. In this paper, we describe ScenarioMIP's objectives, experimental design, and its relation to other activities within CMIP6. The ScenarioMIP design is one component of a larger scenario process that aims to facilitate a wide range of integrated studies across the climate science, integrated assessment modeling, and impacts, adaptation, and vulnerability communities, and will form an important part of the evidence base in the forthcoming Intergovernmental Panel on Climate Change (IPCC) assessments. At the same time, it will provide the basis for investigating a number of targeted science and policy questions that are especially relevant to scenario-based analysis, including the role of specific forcings such as land use and aerosols, the effect of a peak and decline in forcing, the consequences of scenarios that limit warming to below 2 °C, the relative contributions to uncertainty from scenarios, climate models, and internal variability, and long-term climate system outcomes beyond the 21st century. To serve this wide range of scientific communities and address these questions, a design has been identified consisting of eight alternative 21st century scenarios plus one large initial condition ensemble and a set of long-term extensions, divided into two tiers defined by relative priority. Some of these scenarios will also provide a basis for variants planned to be run in other CMIP6-Endorsed MIPs to investigate questions related to specific forcings. Harmonized, spatially explicit emissions and land use scenarios generated with integrated assessment models will be provided to participating climate modeling groups by late 2016, with the climate model simulations run within the 2017–2018 time frame, and output from the climate model projections made available and analyses performed over the 2018–2020 period.

scholarly journals High Resolution Model Intercomparison Project (HighResMIP)

2016 ◽  
Author(s):  
R. J. Haarsma ◽  
M. Roberts ◽  
P. L. Vidale ◽  
C. A. Senior ◽  
A. Bellucci ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Computational Cost ◽  
Horizontal Resolution ◽  
Small Scale ◽  
Model Intercomparison ◽  
Resolution Model ◽  
Intercomparison Project ◽  
High Resolution Model ◽  
The Impact

Abstract. Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. The role of enhanced horizontal resolution in improved process representation in all components of the climate system is of growing interest, particularly as some recent simulations suggest the possibility for significant changes in both large-scale aspects of circulation, as well as improvements in small-scale processes and extremes. However, such high resolution global simulations at climate time scales, with resolutions of at least 50 km in the atmosphere and 0.25° in the ocean, have been performed at relatively few research centers and generally without overall coordination, primarily due to their computational cost. Assessing the robustness of the response of simulated climate to model resolution requires a large multi-model ensemble using a coordinated set of experiments. The Coupled Model Intercomparison Project 6 (CMIP6) is the ideal framework within which to conduct such a study, due to the strong link to models being developed for the CMIP DECK experiments and other MIPs. Increases in High Performance Computing (HPC) resources, as well as the revised experimental design for CMIP6, now enables a detailed investigation of the impact of increased resolution up to synoptic weather scales on the simulated mean climate and its variability. The High Resolution Model Intercomparison Project (HighResMIP) presented in this paper applies, for the first time, a multi-model approach to the systematic investigation of the impact of horizontal resolution. A coordinated set of experiments has been designed to assess both a standard and an enhanced horizontal resolution simulation in the atmosphere and ocean. The set of HighResMIP experiments is divided into three tiers consisting of atmosphere-only and coupled runs and spanning the period 1950-2050, with the possibility to extend to 2100, together with some additional targeted experiments. This paper describes the experimental set-up of HighResMIP, the analysis plan, the connection with the other CMIP6 endorsed MIPs, as well as the DECK and CMIP6 historical simulation. HighResMIP thereby focuses on one of the CMIP6 broad questions: “what are the origins and consequences of systematic model biases?”, but we also discuss how it addresses the World Climate Research Program (WCRP) grand challenges.

scholarly journals Bias-corrected climate projections for South Asia from Coupled Model Intercomparison Project-6

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Vimal Mishra ◽  
Udit Bhatia ◽  
Amar Deep Tiwari
Keyword(s):  
Climate Change ◽  
Impact Assessment ◽  
South Asia ◽  
General Circulation ◽  
Coupled Model ◽  
General Circulation Models ◽  
River Basins ◽  
Climate Projections ◽  
Model Intercomparison ◽  
Intercomparison Project

Abstract Climate change is likely to pose enormous challenges for agriculture, water resources, infrastructure, and livelihood of millions of people living in South Asia. Here, we develop daily bias-corrected data of precipitation, maximum and minimum temperatures at 0.25° spatial resolution for South Asia (India, Pakistan, Bangladesh, Nepal, Bhutan, and Sri Lanka) and 18 river basins located in the Indian sub-continent. The bias-corrected dataset is developed using Empirical Quantile Mapping (EQM) for the historic (1951–2014) and projected (2015–2100) climate for the four scenarios (SSP126, SSP245, SSP370, SSP585) using output from 13 General Circulation Models (GCMs) from Coupled Model Intercomparison Project-6 (CMIP6). The bias-corrected dataset was evaluated against the observations for both mean and extremes of precipitation, maximum and minimum temperatures. Bias corrected projections from 13 CMIP6-GCMs project a warmer (3–5°C) and wetter (13–30%) climate in South Asia in the 21st century. The bias-corrected projections from CMIP6-GCMs can be used for climate change impact assessment in South Asia and hydrologic impact assessment in the sub-continental river basins.

scholarly journals WATCH: Current Knowledge of the Terrestrial Global Water Cycle

2011 ◽  
Vol 12 (6) ◽  
pp. 1149-1156 ◽  
Author(s):  
Richard Harding ◽  
Martin Best ◽  
Eleanor Blyth ◽  
Stefan Hagemann ◽  
Pavel Kabat ◽  
...  
Keyword(s):  
Land Surface ◽  
Water Cycle ◽  
Current Knowledge ◽  
Water Model ◽  
Grand Challenge ◽  
Global Water Cycle ◽  
Wide Range ◽  
Surface Models ◽  
Intercomparison Project ◽  
Global Water

Abstract Water-related impacts are among the most important consequences of increasing greenhouse gas concentrations. Changes in the global water cycle will also impact the carbon and nutrient cycles and vegetation patterns. There is already some evidence of increasing severity of floods and droughts and increasing water scarcity linked to increasing greenhouse gases. So far, however, the most important impacts on water resources are the direct interventions by humans, such as dams, water extractions, and river channel modifications. The Water and Global Change (WATCH) project is a major international initiative to bring together climate and water scientists to better understand the current and future water cycle. This paper summarizes the underlying motivation for the WATCH project and the major results from a series of papers published or soon to be published in the Journal of Hydrometeorology WATCH special collection. At its core is the Water Model Intercomparison Project (WaterMIP), which brings together a wide range of global hydrological and land surface models run with consistent driving data. It is clear that we still have considerable uncertainties in the future climate drivers and in how the river systems will respond to these changes. There is a grand challenge to the hydrological and climate communities to both reduce these uncertainties and communicate them to a wider society.

Do high resolution GCMs overestimate precipitation over land?

2020 ◽  
Author(s):  
Omar Müller ◽  
Pier Luigi Vidale ◽  
Patrick McGuire ◽  
Benoît Vannière ◽  
Reinhard Schiemann ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
River Discharge ◽  
Hydrological Cycle ◽  
Transport Model ◽  
Meteorological Data ◽  
Grid Point ◽  
Climate Simulation ◽  
Surface Model ◽  
Orographic Precipitation

<p>Previous studies showed that high resolution GCMs overestimate land precipitation when compared against gridded observations or reanalysis (Demory et al. 2014, Vannière et al. 2019). In particular, grid point models (eg. HadGEM3) show a significant increase of precipitation on regions dominated by complex orography, where the scarcity of gauge stations increase the uncertainty of gridded observations. The goal of this work is to assess the effect of such differences in precipitation on river discharge, considering it as an integrator of the water balance at catchment scale. A set of JULES and CLM simulations have been conducted turning rivers on with Total Runoff Integrating Pathways (TRIP) and the River Transport Model (RTM) respectively. The simulations form three ensembles for each land surface model (LSM) which main difference is given by the forcing dataset. The forcings are WFDEI (reanalysis), LR (~1° resolution in meteorological data from GCMs) and HR (~0.25° resolution in meteorological data from GCMs). These ensembles are evaluated in a set of 280 catchments distributed around the world.</p><p>In terms of correlation between simulated and observed river discharge observations, the results show that LSMs forced by reanalysis have higher performance than LSMs forced by GCMs as expected. In terms of biases, the river discharge is underestimated in eight out of eleven major basins when LSMs are forced by reanalysis. On those basins, the extra precipitation estimated by GCMs help to simulate an amount of river discharge closer to observations (Eg. Yenisey and Lena). Moreover, 37 small basins with a strong component of orographic precipitation over the Andes, the Rocky Mountains, the Alps and in the Maritime Continent were evaluated. In most cases HR offers notably better results than LR and WFDEI, suggesting that high resolution models produce orographic precipitation in the correct place and time.</p><p>In future works offline TRIP simulations will be carried out directly forced by runoff and subsurface runoff from GCMs. It will allow to discard errors in evapotranspiration produced by JULES or CLM when they are used to simulate river discharge. This work is part of the European Process-based climate sIMulation: AdVances in high resolution modelling and European climate Risk Assessment (PRIMAVERA) Project. PRIMAVERA is a collaboration between 19 funded by the European Union’s Horizon 2020 Research & Innovation Programme.</p><p>Demory, M. E., Vidale, P. L., Roberts, M. J., Berrisford, P., Strachan, J., Schiemann, R., & Mizielinski, M. S. (2014). The role of horizontal resolution in simulating drivers of the global hydrological cycle. CLIM DYNAM, 42(7-8), 2201-2225.</p><p>Vannière, B., Demory, M. E., Vidale, P. L., Schiemann, R., Roberts, M. J., Roberts, C. D., ... & Senan, R. (2018). Multi-model evaluation of the sensitivity of the global energy budget and hydrological cycle to resolution. CLIM DYNAM, 1-30.</p>

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