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>

scholarly journals Validation of a new SAFRAN-based gridded precipitation product for Spain and comparisons to Spain02 and ERA-Interim

2017 ◽  
Vol 21 (4) ◽  
pp. 2187-2201 ◽  
Author(s):  
Pere Quintana-Seguí ◽  
Marco Turco ◽  
Sixto Herrera ◽  
Gonzalo Miguez-Macho
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Interpolation Method ◽  
Rain Gauge ◽  
Surface Model ◽  
Low Resolution ◽  
Precipitation Events

Abstract. Offline land surface model (LSM) simulations are useful for studying the continental hydrological cycle. Because of the nonlinearities in the models, the results are very sensitive to the quality of the meteorological forcing; thus, high-quality gridded datasets of screen-level meteorological variables are needed. Precipitation datasets are particularly difficult to produce due to the inherent spatial and temporal heterogeneity of that variable. They do, however, have a large impact on the simulations, and it is thus necessary to carefully evaluate their quality in great detail. This paper reports the quality of two high-resolution precipitation datasets for Spain at the daily time scale: the new SAFRAN-based dataset and Spain02. SAFRAN is a meteorological analysis system that was designed to force LSMs and has recently been extended to the entirety of Spain for a long period of time (1979/1980–2013/2014). Spain02 is a daily precipitation dataset for Spain and was created mainly to validate regional climate models. In addition, ERA-Interim is included in the comparison to show the differences between local high-resolution and global low-resolution products. The study compares the different precipitation analyses with rain gauge data and assesses their temporal and spatial similarities to the observations. The validation of SAFRAN with independent data shows that this is a robust product. SAFRAN and Spain02 have very similar scores, although the latter slightly surpasses the former. The scores are robust with altitude and throughout the year, save perhaps in summer when a diminished skill is observed. As expected, SAFRAN and Spain02 perform better than ERA-Interim, which has difficulty capturing the effects of the relief on precipitation due to its low resolution. However, ERA-Interim reproduces spells remarkably well in contrast to the low skill shown by the high-resolution products. The high-resolution gridded products overestimate the number of precipitation days, which is a problem that affects SAFRAN more than Spain02 and is likely caused by the interpolation method. Both SAFRAN and Spain02 underestimate high precipitation events, but SAFRAN does so more than Spain02. The overestimation of low precipitation events and the underestimation of intense episodes will probably have hydrological consequences once the data are used to force a land surface or hydrological model.

Hydrological impact of the new ECMWF multi-layer snow scheme

2021 ◽  
Author(s):  
Gabriele Arduini ◽  
Ervin Zsoter ◽  
Hannah Cloke ◽  
Elisabeth Stephens ◽  
Christel Prudhomme
Keyword(s):  
Land Surface ◽  
River Discharge ◽  
Hydrological Cycle ◽  
Land Surface Model ◽  
Single Layer ◽  
Observation Time ◽  
Surface Model ◽  
Earth System ◽  
Hydrological Impact ◽  
Energy And Momentum

<p>Snow processes, with the water stored in the snowpack and released as snowmelt, are very important components of the water balance, in particular in high latitude and mountain regions. The evolution of the snow cover and the timing of the snow melt can have major impact on river discharge. Land surface models are used in Earth System models to compute exchanges of water, energy and momentum between the atmosphere and the surface underneath, and also to compute other components of the hydrological cycle. In order to improve the snow representation, a new multi-layer snow scheme is under development in the HTESSEL land surface model of the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), to replace the current single-layer snow scheme used in HTESSEL. The new scheme has already been shown to improve snow and 2‐metre temperature, while in this study, the wider hydrological impact is evaluated and documented.</p><p>The analysis is done in the reanalysis context by comparing two ERA5-forced offline HTESSEL experiments. The runoff output of HTESSEL is coupled to the CaMa-Flood hydrodynamic model in order to derive river discharge. The analysis is done globally for the period between 1980-2018. The evaluation was carried out using over 1000 discharge observation time-series with varying catchment size. The hydrological response of the multi-layer snow scheme is generally positive, but in some areas the improvement is not clear and can even be negative with deteriorated signal in river discharge. Further investigation is needed to understand the complex hydrological impact of the new snow scheme, making sure it contributes to an improved description of all hydrological components of the Earth System.</p>

scholarly journals Validation of a new SAFRAN-based gridded precipitation product for Spain and comparisons to Spain02 and ERA-Interim

2016 ◽  
Author(s):  
Pere Quintana-Seguí ◽  
Marco Turco ◽  
Sixto Herrera ◽  
Gonzalo Miguez-Macho
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Interpolation Method ◽  
Surface Model ◽  
Low Resolution ◽  
Skill Scores ◽  
Precipitation Events

Abstract. Offline Land-Surface Model (LSM) simulations are useful for studying the continental hydrological cycle. Because of the nonlinearities in the models, the results are very sensitive to the quality of the meteorological forcing; thus, high-quality gridded datasets of screen-level meteorological variables are needed. Precipitation datasets are particularly difficult to produce due to the inherent spatial and temporal heterogeneity of that variable. They do, however, have a large impact on the simulations, and it is thus necessary to carefully evaluate their quality in great detail. This paper reports the quality of two high-resolution precipitation datasets for Spain at the daily time scale: the new SAFRAN-based dataset and Spain02. SAFRAN is a meteorological analysis system that was designed to force LSMs and has recently been extended to the entirety of Spain for a long period of time (1979/80–2013/14). Spain02 is a daily precipitation dataset for Spain and was created mainly to validate Regional Climate Models. In addition, ERA-Interim is included in the comparison to show the differences between local high-resolution and global low-resolution products. The study compares the different precipitation analyses with rain gauge data and assesses their temporal and spatial similarities to the observations. The results show that SAFRAN and Spain02 have very similar skill scores, although the later has better scores in general. As expected, SAFRAN and Spain02 perform better than ERA-Interim, which has difficulty capturing the effects of the relief on precipitation due to its low resolution. However, ERA-Interim reproduces spells remarkably well, in contrast to the low skill shown by the high-resolution products. The high-resolution gridded products overestimate the number of precipitation days, which is a problem that affects SAFRAN more than Spain02 and is likely caused by the interpolation method. Both SAFRAN and Spain02 underestimate high precipitation events, but SAFRAN does so more than Spain02. The overestimation of low precipitation events and the underestimation of intense episodes will probably have hydrological consequences once the data are used to force a land surface or hydrological model.

scholarly journals Using Gravity Recovery and Climate Experiment data to derive corrections to precipitation data sets and improve modelled snow mass at high latitudes

2020 ◽  
Vol 24 (4) ◽  
pp. 1763-1779
Author(s):  
Emma L. Robinson ◽  
Douglas B. Clark
Keyword(s):  
Land Surface ◽  
River Discharge ◽  
Meteorological Data ◽  
Land Surface Model ◽  
Cold Season ◽  
Surface Model ◽  
Data Sets ◽  
Seasonal Maximum ◽  
Basin Scale ◽  
Gravity Recovery

Abstract. The amount of lying snow calculated by a land surface model depends in part on the amount of snowfall in the meteorological data that are used to drive the model. We show that commonly used data sets differ in the amount of snowfall, and more generally precipitation, over four large Arctic basins. An independent estimate of the cold-season precipitation is obtained by combining water balance information from the Gravity Recovery and Climate Experiment (GRACE) with estimates of evaporation and river discharge and is generally higher than that estimated by four commonly used meteorological data sets. We use the Joint UK Land Environment Simulator (JULES) land surface model to calculate the snow water equivalent (SWE) over the four basins. The modelled seasonal maximum SWE is 38 % less than observation-based estimates on average, and the modelled basin discharge is significantly underestimated, consistent with the lack of snowfall. We use the GRACE-derived estimate of precipitation to define per-basin scale factors that are applied to the driving data and increase the amount of cold-season precipitation by 28 % on average. In turn this increases the modelled seasonal maximum SWE by 30 %, although this is still underestimated compared to observations by 19 % on average. A correction for the undercatch of precipitation by gauges is compared with the the GRACE-derived correction. Undercatch correction increases the amount of cold-season precipitation by 23 % on average, which indicates that some, but not all, of the underestimation can be removed by implementing existing undercatch correction algorithms. However, even undercatch-corrected data sets contain less precipitation than the GRACE-derived estimate in some regions, and it is likely that there are other biases that are not currently accounted for in gridded meteorological data sets. This study shows that revised estimates of precipitation can lead to improved modelling of SWE, but much more modest improvements are found in modelled river discharge. By providing methods to better define the precipitation inputs to the system, the current study paves the way for subsequent work on key hydrological processes in high-latitude basins.

scholarly journals Using GRACE to derive corrections to precipitation data sets and improve modelled snow mass at high latitudes

2019 ◽  
Author(s):  
Emma L. Robinson ◽  
Douglas B. Clark
Keyword(s):  
Land Surface ◽  
River Discharge ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Land Surface Model ◽  
Cold Season ◽  
Surface Model ◽  
Data Sets ◽  
Seasonal Maximum ◽  
Basin Scale

Abstract. The amount of lying snow calculated by a land surface model depends in part on the amount of snowfall in the meteorological data that are used to drive the model. We show that commonly-used data sets differ in the amount of snowfall, and more generally precipitation, over four large Arctic basins. An independent estimate of the cold season precipitation is obtained by combining water balance information from the Gravity Recovery and Climate Experiment (GRACE) with estimates of evaporation and river discharge, and is generally higher than that estimated by four commonly-used meteorological data sets. We use the Joint UK Land Environment Simulator (JULES) land surface model to calculate the snow water equivalent (SWE) over the four basins. The modelled seasonal maximum SWE is 38 % less than observation-based estimates on average and the modelled basin discharge is significantly underestimated, consistent with the lack of snowfall. We use the GRACE-derived estimate of precipitation to define per-basin scale factors that are applied to the driving data and increase the amount of cold season precipitation by 28 % on average. In turn this increases the modelled seasonal maximum SWE by 30 %, although this is still underestimated compared to observations by 19 % on average. A correction for undercatch of precipitation by gauges is compared with the the GRACE-derived correction. Undercatch correction increases the amount of cold season precipitation by 23 % on average, which indicates that some, but not all, of the underestimation can be removed by implementing existing undercatch correction algorithms. However, even undercatch-corrected data sets contain less precipitation than the GRACE-derived estimate in some regions, and it is likely that there are other biases that that are not currently accounted for in gridded meteorological data sets. This study shows that revised estimates of precipitation can lead to improved modelling of SWE, but much more modest improvements are found in modelled river discharge. By providing methods to better define the precipitation inputs to the system, the current study paves the way for subsequent work on key hydrological processes in high-latitude basins.

scholarly journals Integrated validation of assimilating satellite derived observations over France using a hydrological model

2016 ◽  
Author(s):  
D. Fairbairn ◽  
A. L. Barbu ◽  
A. Napoly ◽  
C. Albergel ◽  
J.-F. Mahfouf ◽  
...  
Keyword(s):  
Land Surface ◽  
Radiative Forcing ◽  
River Discharge ◽  
Hydrological Model ◽  
Hydrological Cycle ◽  
Wave Radiation ◽  
Surface Model ◽  
Model Errors ◽  
Area Index ◽  
Observation Operator

Abstract. This study assesses the impacts of assimilating surface soil moisture (SSM) and leaf area index (LAI) observations on river discharge using the SAFRAN-ISBA-MODCOU (SIM) hydrological model. The SIM hydrological model consists of three stages: (1) An atmospheric reanalysis (SAFRAN) over France, which forces (2) a land surface model (ISBA-A-gs), which then provides drainage and runoff inputs to (3) a hydrogeological model (MODCOU). The river discharge from MODCOU is validated using observed river discharge over France from over 500 gauges. The SAFRAN forcing underestimates direct short-wave and long-wave radiation by approximately 5% averaged over France. The ISBA-A-gs model also significantly underestimates the grassland LAI compared with satellite retrievals during winter dormancy. These differences result in an under-estimation (overestimation) of evapotranspiration (drainage and runoff). The excess water flowing into the rivers and aquifers contributes to an overestimation of the SIM discharge. We attempted to resolve these problems by performing the following experiments: (i) a correction of the minimum LAI model parameter for grasslands, (ii) a bias-correction of the model radiative forcing, (iii) the assimilation of LAI observations and (iv) the assimilation of SSM and LAI observations. The data assimilation for (iii) and (iv) was done with a simplified extended Kalman filter (SEKF), which uses finite differences in the observation operator Jacobians to relate the observations to the model variables. Experiments (i) and (ii) improved the average SIM Nash scores by about 12 % and 20 % respectively. Experiment (iii) reduced the LAI phase errors in ISBA-A-gs but only slightly improved the discharge Nash effciency of SIM (by just 2 %). In contrast, experiment (iv) resulted in spurious increases in drainage and runoff, which degraded the discharge Nash effciency by about 35%. The poor performance of the SEKF is an artifact of the observation operator Jacobians. These Jacobians are dampened when the soil is saturated and when the vegetation is dormant, which leads to positive biases in drainage/runoff and insuffcient corrections to the LAI minimum, respectively. This motivates the development of a DA method that can take into account model errors and atmospheric forcing errors. The results also highlight the important role that vegetation plays on the hydrological cycle. It is recommended that a spatially variable LAI minimum parameter be introduced into ISBA-A-gs based on the lowest LAI values derived from satellite observations.

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 A High-Resolution National-Scale Hydrologic Forecast System from a Global Ensemble Land Surface Model

2016 ◽  
Vol 52 (4) ◽  
pp. 950-964 ◽  
Author(s):  
Alan D. Snow ◽  
Scott D. Christensen ◽  
Nathan R. Swain ◽  
E. James Nelson ◽  
Daniel P. Ames ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
National Scale ◽  
Forecast System ◽  
Hydrologic Forecast

A novel application of an adaptive surrogate-based modeling optimization (ASMO) for the Community Territory System Model (CTSM) in Alaska

2021 ◽  
Author(s):  
Yifan Cheng ◽  
Andrew Newman ◽  
Sean Swenson ◽  
David Lawrence ◽  
Anthony Craig ◽  
...  
Keyword(s):  
High Resolution ◽  
River Basin ◽  
Land Surface ◽  
Computational Cost ◽  
Reanalysis Data ◽  
Freshwater Ecosystems ◽  
System Model ◽  
Surface Model ◽  
Yukon River ◽  
Yukon River Basin

<p>Climate-induced changes in snow cover, river flow, and freshwater ecosystems will greatly affect the indigenous groups in the Alaska and Yukon River Basin. To support policy-making on climate adaptation and mitigation for these underrepresented groups, an ongoing interdisciplinary effort is being made to combine Indigenous Knowledge with western science (https://www.colorado.edu/research/arctic-rivers/).</p><p>A foundational component of this project is a high fidelity representation of the aforementioned land surface processes. To this end, we aim to obtain a set of reliable high-resolution parameters for the Community Territory System Model (CTSM) for the continental scale domain of Alaska and the entire Yukon River Basin, which will be used in climate change simulations. CTSM is a complex, physically based state-of-the-science land surface model that includes complex vegetation and canopy representation, a multi-layer snow model, as well as hydrology and frozen soil physics necessary for the representation of streamflow and permafrost. Two modifications to the default CTSM configuration were made. First, we used CTSM that is implemented with hillslope hydrology to better capture the fine-scale hydrologic spatial heterogeneity in complex terrain. Second, we updated the input soil textures and organic carbon in CTSM using the high-resolution SoilGrid dataset.</p><p>In this study, we performed a multi-objective optimization on snow and streamflow metrics using an adaptive surrogate-based modeling optimization (ASMO). ASMO permits optimization of complex land-surface models over large domains through the use of surrogate models to minimize the computational cost of running the full model for every parameter combination. We ran CTSM at a spatial resolution of 1/24<sup>th</sup> degree and a temporal resolution of one hour using the ERA5 reanalysis data as the meteorological forcings. The ERA5 reanalysis data were bias-corrected to account for the orographic effects. We will discuss the ASMO-CTSM coupling workflow, performance characteristics of the optimization (e.g., computational cost, iterations), and comparisons of the default configuration and optimized model performance.</p>

scholarly journals Continuous high-resolution midlatitude-belt simulations for July–August 2013 with WRF

2017 ◽  
Vol 10 (5) ◽  
pp. 2031-2055 ◽  
Author(s):  
Thomas Schwitalla ◽  
Hans-Stefan Bauer ◽  
Volker Wulfmeyer ◽  
Kirsten Warrach-Sagi
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Temperature Fields ◽  
Surface Model ◽  
Limited Area ◽  
Data Set ◽  
Latitude Belt ◽  
Simulated Precipitation ◽  
The Impact

Abstract. Increasing computational resources and the demands of impact modelers, stake holders, and society envision seasonal and climate simulations with the convection-permitting resolution. So far such a resolution is only achieved with a limited-area model whose results are impacted by zonal and meridional boundaries. Here, we present the setup of a latitude-belt domain that reduces disturbances originating from the western and eastern boundaries and therefore allows for studying the impact of model resolution and physical parameterization. The Weather Research and Forecasting (WRF) model coupled to the NOAH land–surface model was operated during July and August 2013 at two different horizontal resolutions, namely 0.03 (HIRES) and 0.12° (LOWRES). Both simulations were forced by the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis data at the northern and southern domain boundaries, and the high-resolution Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) data at the sea surface.The simulations are compared to the operational ECMWF analysis for the representation of large-scale features. To analyze the simulated precipitation, the operational ECMWF forecast, the CPC MORPHing (CMORPH), and the ENSEMBLES gridded observation precipitation data set (E-OBS) were used as references.Analyzing pressure, geopotential height, wind, and temperature fields as well as precipitation revealed (1) a benefit from the higher resolution concerning the reduction of monthly biases, root mean square error, and an improved Pearson skill score, and (2) deficiencies in the physical parameterizations leading to notable biases in distinct regions like the polar Atlantic for the LOWRES simulation, the North Pacific, and Inner Mongolia for both resolutions.In summary, the application of a latitude belt on a convection-permitting resolution shows promising results that are beneficial for future seasonal forecasting.

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