Intercomparison of terrestrial water budgets in EURO-CORDEX and TSMP evaluation runs

2020 ◽  
Author(s):  
Mohamed Eltahan ◽  
Klaus Goergen ◽  
Carina Furusho-Percot ◽  
Stefan Kollet
Keyword(s):  
Land Surface ◽  
Water Budget ◽  
Climate Models ◽  
Water Cycle ◽  
Weather Prediction ◽  
Regional Climate ◽  
Shallow Groundwater ◽  
Surface Model ◽  
Frequency Distributions ◽  
Terrestrial Water

<p>Water is one of Earth’s most important geo-ecosystem components. Here we present an evaluation of water cycle components using 12 EURO-CORDEX Regional Climate Models (RCMs) and the Terrestrial Systems Modeling Platform (TSMP) from ERA-Interim driven evaluation runs. Unlike the other RCMs, TSMP provides an <span>integrated</span> representation of the terrestrial water cycle by coupling the numerical weather prediction model COSMO, the land surface model CLM and the surface-subsurface hydrological model ParFlow, which simulates shallow groundwater states and fluxes. The study analyses precipitation (P), evapotranspiration (E), runoff (R), and terrestrial water storage (TWS=P-E-R) at a 0.11degree spatial resolution (about 12km) on EUR-11 CORDEX grid from 1996 to 2008. As reference datasets, we use ERA5 reanalysis to <span>represent</span> the complete terrestrial water budget, <span>as well as </span>the E-OBS, GLEAM and E-Run datasets for precipitation, evapotranspiration and runoff, respectively. The terrestrial water budget is investigated for twenty catchments over Europe (Guadalquivir, Guadiana, Tagus, Douro, Ebro, Garonne, Rhone, Po, Seine, Rhine, Loire, Maas, Weser, Elbe, Oder, Vistuala, Danube, Dniester, Dnieper, and Neman). Annual cycles, seasonal variations, empirical frequency distributions, spatial distributions for the water cycle components and budgets over the catchments are assessed. The analysis <span>demonstrates</span> the capability of the RCMs and TSMP to reproduce the overall <span>characteristics of the</span> water cycle over the EURO-CORDEX domain<span>, which is a prerequisite if, e.g., climate change projections with the CORDEX RCMs or TSMP are to be used for vulnerability, impacts, and adaptation studies.</span></p>

scholarly journals Pan-European groundwater to atmosphere terrestrial systems climatology from a physically consistent simulation

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Carina Furusho-Percot ◽  
Klaus Goergen ◽  
Carl Hartick ◽  
Ketan Kulkarni ◽  
Jessica Keune ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Water Cycle ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Data Set ◽  
Groundwater Dynamics ◽  
Terrestrial System ◽  
Terrestrial Water

AbstractApplying the Terrestrial Systems Modeling Platform, TSMP, this study provides the first simulated long-term (1996–2018), high-resolution (~12.5 km) terrestrial system climatology over Europe, which comprises variables from groundwater across the land surface to the top of the atmosphere (G2A). The data set offers an unprecedented opportunity to test hypotheses related to short- and long-range feedback processes in space and time between the different interacting compartments of the terrestrial system. The physical consistency of simulated states and fluxes in the terrestrial system constitutes the uniqueness of the data set: while most regional climate models (RCMs) have a tendency to simplify the soil moisture and groundwater representation, TSMP explicitly simulates a full 3D soil- and groundwater dynamics, closing the terrestrial water cycle from G2A. As anthopogenic impacts are excluded, the dataset may serve as a near-natural reference for global change simulations including human water use and climate change. The data set is available as netCDF files for the pan-European EURO-CORDEX domain.

scholarly journals Validation of a High Resolution Numerical Weather Prediction Land Surface Scheme using Catchment Water Balances

2021 ◽  
Author(s):  
Daniel Regenass ◽  
Linda Schlemmer ◽  
Oliver Fuhrer ◽  
Jean-Marie Bettems ◽  
Marco Arpagaus ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Land Surface ◽  
Climate Models ◽  
Water Cycle ◽  
Weather Prediction ◽  
The State ◽  
Mass Balances ◽  
Numerical Weather ◽  
Terrestrial Water ◽  
Surface Scheme

AbstractAn adequate representation of the interaction between the land surface and the atmosphere is critical for both numerical weather prediction and climate models. The surface energy and mass balances are tightly coupled to the terrestrial water cycle, mainly through the state of soil moisture. An inadequate representation of the terrestrial water cycle will deteriorate the state of the land surface model and introduce biases to the atmospheric model. The validation of land-surface models is challenging, as there are very few observations and the soil is highly heterogeneous. In this paper, a validation framework for land-surface schemes based on catchment mass balances is presented. The main focus of our development lies in the application to kilometer-resolution numerical weather prediction and climate models, although the approach is scalable in both space and time. The methodology combines information from multiple observation-based datasets. Observational uncertainties are estimated by using independent sets of observations. It is shown that the combination of observation-based datasets and river discharge measurements close the water balance fairly well for the chosen catchments. As a showcase application, the framework is then applied to compare and validate four different versions ofTERRAML, the land-surface scheme of the COSMO numerical weather prediction and climate model over five mesoscale catchments in Switzerland ranging from 105 km2 to 1713 km2. Despite large observational uncertainties, validation results clearly suggest that errors in terrestrial storage changes are closely linked to errors in runoff generation and emphasize the crucial role of infiltration processes.

scholarly journals Diagnosing hydrological limitations of a land surface model: application of JULES to a deep-groundwater chalk basin

2016 ◽  
Vol 20 (1) ◽  
pp. 143-159 ◽  
Author(s):  
N. Le Vine ◽  
A. Butler ◽  
N. McIntyre ◽  
C. Jackson
Keyword(s):  
Land Surface ◽  
Expert Knowledge ◽  
Water Cycle ◽  
Land Surface Model ◽  
Surface Model ◽  
Deep Groundwater ◽  
Resolution Model ◽  
Terrestrial Water ◽  
Water Redistribution ◽  
Types Of Information

Abstract. Land surface models (LSMs) are prospective starting points to develop a global hyper-resolution model of the terrestrial water, energy, and biogeochemical cycles. However, there are some fundamental limitations of LSMs related to how meaningfully hydrological fluxes and stores are represented. A diagnostic approach to model evaluation and improvement is taken here that exploits hydrological expert knowledge to detect LSM inadequacies through consideration of the major behavioural functions of a hydrological system: overall water balance, vertical water redistribution in the unsaturated zone, temporal water redistribution, and spatial water redistribution over the catchment's groundwater and surface-water systems. Three types of information are utilized to improve the model's hydrology: (a) observations, (b) information about expected response from regionalized data, and (c) information from an independent physics-based model. The study considers the JULES (Joint UK Land Environmental Simulator) LSM applied to a deep-groundwater chalk catchment in the UK. The diagnosed hydrological limitations and the proposed ways to address them are indicative of the challenges faced while transitioning to a global high resolution model of the water cycle.

scholarly journals The Met Office Unified Model Global Atmosphere 4.0 and JULES Global Land 4.0 configurations

2014 ◽  
Vol 7 (1) ◽  
pp. 361-386 ◽  
Author(s):  
D. N. Walters ◽  
K. D. Williams ◽  
I. A. Boutle ◽  
A. C. Bushell ◽  
J. M. Edwards ◽  
...  
Keyword(s):  
Land Surface ◽  
Weather Prediction ◽  
Regional Climate ◽  
Land Surface Model ◽  
Unified Model ◽  
Surface Model ◽  
Development Cycle ◽  
Global Land ◽  
Overall Performance ◽  
Ongoing Development

Abstract. We describe Global Atmosphere 4.0 (GA4.0) and Global Land 4.0 (GL4.0): configurations of the Met Office Unified Model and JULES (Joint UK Land Environment Simulator) community land surface model developed for use in global and regional climate research and weather prediction activities. GA4.0 and GL4.0 are based on the previous GA3.0 and GL3.0 configurations, with the inclusion of developments made by the Met Office and its collaborators during its annual development cycle. This paper provides a comprehensive technical and scientific description of GA4.0 and GL4.0 as well as details of how these differ from their predecessors. We also present the results of some initial evaluations of their performance. Overall, performance is comparable with that of GA3.0/GL3.0; the updated configurations include improvements to the science of several parametrisation schemes, however, and will form a baseline for further ongoing development.

Data assimilation of GRACE terrestrial water storage to improve sea level rise estimates and isolate anthropogenic influences

2021 ◽  
Author(s):  
Ann Scheliga ◽  
Manuela Girotto
Keyword(s):  
Data Assimilation ◽  
Sea Level Rise ◽  
Sea Level ◽  
Land Surface ◽  
Water Budget ◽  
Water Storage ◽  
Surface Model ◽  
Terrestrial Water Storage ◽  
Terrestrial Water ◽  
Land Hydrology

<p>Sea level rise (SLR) projections rely on the accurate and precise closure of Earth’s water budget. The Gravity Recovery and Climate Experiment (GRACE) mission has provided global-coverage observations of terrestrial water storage (TWS) anomalies that improve accounting of ice and land hydrology changes and how these changes contribute to sea level rise. The contribution of land hydrology TWS changes to sea level rise is much smaller and less certain than contributions from glacial melt and thermal expansion. Although land hydrology TWS plays a smaller role, it is still important to investigate to improve the precision of the overall global water budget. This study analyzes how data assimilation techniques improve estimates of the land hydrology contribution to sea level rise. To achieve this, three global TWS datasets were analyzed: (1) GRACE TWS observations alone, (2) TWS estimates from the model-only simulation using Catchment Land Surface Model, and (3) TWS estimates from a data assimilation product of (1) and (2). We compared the data assimilation product with the GRACE observations alone and the model-only simulation to isolate the contribution to sea level rise from anthropogenic activities. We assumed a balanced water budget between land hydrology and the ocean, thus changes in global TWS are considered equal and opposite to sea level rise contribution.  Over the period of 2003-2016, we found sea level rise contributions from each dataset of +0.35 mm SLR eq/yr for GRACE, -0.34 mm SLR eq/yr for model-only, and a +0.09 mm SLR eq/yr for DA (reported as the mean linear trend). Our results indicate that the model-only simulation is not capturing important hydrologic processes. These are likely anthropogenic driven, indicating direct anthropogenic and climate-driven TWS changes play a substantial role in TWS contribution to SLR.</p>

scholarly journals A Statistical–Dynamical Methodology to Downscale Regional Climate Projections to Urban Scale

2020 ◽  
Vol 59 (6) ◽  
pp. 1109-1123 ◽  
Author(s):  
François DuchÊne ◽  
Bert Van Schaeybroeck ◽  
Steven Caluwaerts ◽  
Rozemien De Troch ◽  
Rafiq Hamdi ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Heat Waves ◽  
Regional Climate ◽  
Wave Number ◽  
Surface Model ◽  
Climate Projections ◽  
Short Period ◽  
The Impact

AbstractThe demand of city planners for quantitative information on the impact of climate change on the urban environment is increasing. However, such information is usually extracted from decadelong climate projections generated with global or regional climate models (RCMs). Because of their coarse resolution and unsuitable physical parameterization, however, their model output is not adequate to be used at city scale. A full dynamical downscaling to city level, on the other hand, is computationally too expensive for climatological time scales. A statistical–dynamical computationally inexpensive method is therefore proposed that approximates well the behavior of the full dynamical downscaling approach. The approach downscales RCM simulations using the combination of an RCM at high resolution (H-RES) and a land surface model (LSM). The method involves the setup of a database of urban signatures by running an H-RES RCM with and without urban parameterization for a relatively short period. Using an analog approach, these signatures are first selectively added to the long-term RCM data, which are then used as forcing for an LSM using an urban parameterization in a stand-alone mode. A comparison with a full dynamical downscaling approach is presented for the city of Brussels, Belgium, for 30 summers with the combined ALADIN–AROME model (ALARO-0) coupled to the Surface Externalisée model (SURFEX) as H-RES RCM and SURFEX as LSM. The average bias of the nocturnal urban heat island during heat waves is vanishingly small, and the RMSE is strongly reduced. Not only is the statistical–dynamical approach able to correct the heat-wave number and intensities, it can also improve intervariable correlations and multivariate and temporally correlated indices, such as Humidex.

scholarly journals The Met Office Unified Model Global Atmosphere 4.0 and JULES Global Land 4.0 configurations

2013 ◽  
Vol 6 (2) ◽  
pp. 2813-2881 ◽  
Author(s):  
D. N. Walters ◽  
K. D. Williams ◽  
I. A. Boutle ◽  
A. C. Bushell ◽  
J. M. Edwards ◽  
...  
Keyword(s):  
Land Surface ◽  
Weather Prediction ◽  
Regional Climate ◽  
Land Surface Model ◽  
Unified Model ◽  
Surface Model ◽  
Development Cycle ◽  
Global Land ◽  
Overall Performance ◽  
Ongoing Development

Abstract. We describe Global Atmosphere 4.0 (GA4.0) and Global Land 4.0 (GL4.0): configurations of the Met Office Unified Model and JULES community land surface model developed for use in global and regional climate research and weather prediction activities. GA4.0 and GL4.0 are based on the previous GA3.0 and GL3.0 configurations, with the inclusion of developments made by the Met Office and its collaborators during its annual development cycle. This paper provides a comprehensive technical and scientific description of GA4.0 and GL4.0 as well as details of how these differ from their predecessors. We also present the results of some initial evaluations of their performance. These show that, overall, performance is comparable with that of GA3.0/GL3.0; the updated configurations do, however, include improvements to the science of several parametrization schemes and will form a baseline for further ongoing development.

scholarly journals Diagnosing hydrological limitations of a Land Surface Model: application of JULES to a deep-groundwater chalk basin

2015 ◽  
Vol 12 (8) ◽  
pp. 7541-7582
Author(s):  
N. Le Vine ◽  
A. Butler ◽  
N. McIntyre ◽  
C. Jackson
Keyword(s):  
Land Surface ◽  
Expert Knowledge ◽  
Water Cycle ◽  
Land Surface Model ◽  
Surface Model ◽  
Deep Groundwater ◽  
Resolution Model ◽  
Terrestrial Water ◽  
Water Redistribution ◽  
Types Of Information

Abstract. Land Surface Models (LSMs) are prospective starting points to develop a global hyper-resolution model of the terrestrial water, energy and biogeochemical cycles. However, there are some fundamental limitations of LSMs related to how meaningfully hydrological fluxes and stores are represented. A diagnostic approach to model evaluation is taken here that exploits hydrological expert knowledge to detect LSM inadequacies through consideration of the major behavioural functions of a hydrological system: overall water balance, vertical water redistribution in the unsaturated zone, temporal water redistribution and spatial water redistribution over the catchment's groundwater and surface water systems. Three types of information are utilised to improve the model's hydrology: (a) observations, (b) information about expected response from regionalised data, and (c) information from an independent physics-based model. The study considers the JULES (Joint UK Land Environmental Simulator) LSM applied to a deep-groundwater chalk catchment in the UK. The diagnosed hydrological limitations and the proposed ways to address them are indicative of the challenges faced while transitioning to a global high resolution model of the water cycle.

Water Cycle Extremes in the GRACE and GRACE-FO Data Record

2020 ◽  
Author(s):  
Matthew Rodell ◽  
Bailing Li
Keyword(s):  
Land Surface ◽  
Water Mass ◽  
Water Cycle ◽  
Land Surface Model ◽  
Surface Model ◽  
Drought Event ◽  
Terrestrial Water Storage ◽  
Data Record ◽  
Terrestrial Water ◽  
Hydrological Droughts

<p>A unique aspect of satellite gravimetry is its ability to quantify changes in all water stored at all depths on and beneath the land surface.  Hence, GRACE and GRACE-FO are well suited for quantifying both hydrological droughts, when terrestrial water storage (TWS) is low, and pluvial events, when TWS is high.  In this study we use GRACE and GRACE-FO data assimilation within a land surface model to fill the 1-year gap between the two missions and to replace other missing data.  We apply a cluster analysis approach to identify the locations and extents of TWS extreme events in resulting data record.  We then rank these events based on their intensity, i.e., the integral of the non-seasonal water mass anomaly over the period of the event.  In this presentation we report on the largest wet and dry events over each continent.  During the period of study, Africa, North America, and Australia each had a wet event with an intensity that exceeded 10,000 km<sup>3</sup> * month, although the 2010-2012 event in Australia can largely be attributed to a depressed baseline TWS during the period caused by the millennial drought.  With 30 more years of data it is probable that the intensity of that drought would have been greater than the recovery and wet event during 2010-2012.  As it stands, the biggest drought event was determined to be one occurred in South America during 2015-2016, with an intensity of over 10,000 km<sup>3</sup> * month.</p>

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