The impact of global reservoir expansion on the present-day climate  

Author(s):  
Inne Vanderkelen ◽  
Nicole P. M. van Lipzig ◽  
William J. Sacks ◽  
David M. Lawrence ◽  
Martyn Clark ◽  
...  

<p>By now, humans have constructed more than 45 000 large reservoirs across the globe, increasing the global lake area with 8%. These reservoirs have large impacts on freshwater processes and resources by impounding continental runoff and altering river flows. So far, the impact of reservoirs on the climate remains largely unknown, as they are typically not represented in current Earth System Models (ESMs). This is remarkable, as two-way interactions between reservoirs and climate are likely to alter hydrological extremes and impact future water availability.</p><p>Here we present the implementation of the role of reservoirs in the Community Terrestrial Systems Model (CTSM), a land surface model, by accounting for the increase in open water surfaces due to reservoir construction throughout the 20<sup>th</sup> century. To this end, we allow lake area to expand in the model, while ensuring that the surface energy and mass balances remain closed. We use reservoir and lake extent  from the state-of-the-art Global Reservoir and Dams (GRanD) and HydroLAKES data sets.</p><p>By conducting both land-only and coupled simulations with CTSM and the Community Earth System Model (CESM), we assess the added value of accounting for reservoir expansion in the land surface model performance and investigate their impacts on the mean climate and extremes. Globally, the effect of reservoirs on temperatures and the surface energy balance is small, but  responses can be substantial locally, in particular for grid cells where reservoirs make up a large fraction. Our results show that reservoirs reduce temperature extremes and moderate the seasonal temperature cycle, by up to -1.5 K (for reservoirs covering > 15% of the grid cell).</p><p>This study is an important step towards incorporating human water management in ESMs.</p><p> </p>

2015 ◽  
Vol 15 (20) ◽  
pp. 29265-29302
Author(s):  
L. Chen ◽  
Y. Li ◽  
F. Chen ◽  
A. Barr ◽  
M. Barlage ◽  
...  

Abstract. In this study, the multi-parameterization version of the Noah land-surface model (Noah-MP) was used to investigate the impact of adding a forest-floor organic soil layer on the simulated surface energy and water cycle components at a boreal aspen forest. The test site selected is BERMS Old Aspen Flux (OAS) field station in central Saskatchewan, Canada. The selection of different parameterization schemes for each process within the current Noah-MP model significantly affected the simulation results. The best combination options without incorporating organic soil is referred as the control experiment (CTL). By including an organic-soil parameterization within the Noah-MP model for the first time, the verification results (OGN) against site show significantly improved performance of the model in surface energy fluxes and hydrology simulation due to the lower thermal conductivity and greater porosity of the organic soil. The effects of including an organic soil layer on soil temperature are not uniform throughout the soil depth and year, and those effects are more prominent in summer and in deep soils. For drought years, the OGN simulation substantially modified the partition between direct soil evaporation and vegetation transpiration. For wet years, the OGN simulated latent heat fluxes are similar to CTL except for spring season where OGN produced less evaporation. The impact of the organic soil on sub-surface runoff is substantive with much higher runoff throughout the season.


2015 ◽  
Vol 8 (6) ◽  
pp. 1857-1876 ◽  
Author(s):  
J. J. Guerrette ◽  
D. K. Henze

Abstract. Here we present the online meteorology and chemistry adjoint and tangent linear model, WRFPLUS-Chem (Weather Research and Forecasting plus chemistry), which incorporates modules to treat boundary layer mixing, emission, aging, dry deposition, and advection of black carbon aerosol. We also develop land surface and surface layer adjoints to account for coupling between radiation and vertical mixing. Model performance is verified against finite difference derivative approximations. A second-order checkpointing scheme is created to reduce computational costs and enable simulations longer than 6 h. The adjoint is coupled to WRFDA-Chem, in order to conduct a sensitivity study of anthropogenic and biomass burning sources throughout California during the 2008 Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) field campaign. A cost-function weighting scheme was devised to reduce the impact of statistically insignificant residual errors in future inverse modeling studies. Results of the sensitivity study show that, for this domain and time period, anthropogenic emissions are overpredicted, while wildfire emission error signs vary spatially. We consider the diurnal variation in emission sensitivities to determine at what time sources should be scaled up or down. Also, adjoint sensitivities for two choices of land surface model (LSM) indicate that emission inversion results would be sensitive to forward model configuration. The tools described here are the first step in conducting four-dimensional variational data assimilation in a coupled meteorology–chemistry model, which will potentially provide new constraints on aerosol precursor emissions and their distributions. Such analyses will be invaluable to assessments of particulate matter health and climate impacts.


Author(s):  
Nemesio Rodriguez-Fernandez ◽  
Patricia de Rosnay ◽  
Clement Albergel ◽  
Philippe Richaume ◽  
Filipe Aires ◽  
...  

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised - Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if regional differences can exist. Experiments performing joint data assimilation (DA) of NNSM, 2 metre air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although, NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m, but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April-September, while NNSM alone has a significant positive effect in July-September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 hours lead time.


2021 ◽  
Author(s):  
Gabriele Arduini ◽  
Ervin Zsoter ◽  
Hannah Cloke ◽  
Elisabeth Stephens ◽  
Christel Prudhomme

<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>


2020 ◽  
Author(s):  
Leqiang Sun ◽  
Stéphane Belair ◽  
Marco Carrera ◽  
Bernard Bilodeau

<p>Canadian Space Agency (CSA) has recently started receiving and processing the images from the recently launched C-band RADARSAT Constellation Mission (RCM). The backscatter and soil moisture retrievals products from the previously launched RADARSAT-2 agree well with both in-situ measurements and surface soil moisture modeled with land surface model Soil, Vegetation, and Snow (SVS). RCM will provide those products at an even better spatial coverage and temporal resolution. In preparation of the potential operational application of RCM products in Canadian Meteorological Center (CMC), this paper presents the scenarios of assimilating either soil moisture retrieval or outright backscatter signal in a 100-meter resolution version of the Canadian Land Data Assimilation System (CaLDAS) on field scale with time interval of three hours. The soil moisture retrieval map was synthesized by extrapolating the regression relationship between in-situ measurements and open loop model output based on soil texture lookup table. Based on this, the backscatter map was then generated with the surface roughness retrieved from RADARSAT-2 images using a modified Integral Equation Model (IEM) model. Bias correction was applied to the Ensemble Kalman filter (EnKF) to mitigate the impact of nonlinear errors introduced by multi-sourced perturbations. Initial results show that the assimilation of backscatter is as effective as assimilating soil moisture retrievals. Compared to open loop, both can improve the analysis of surface moisture, particularly in terms of reducing bias.  </p>


2014 ◽  
Vol 15 (3) ◽  
pp. 1293-1302 ◽  
Author(s):  
M. Tugrul Yilmaz ◽  
Wade T. Crow

Abstract Triple collocation analysis (TCA) enables estimation of error variances for three or more products that retrieve or estimate the same geophysical variable using mutually independent methods. Several statistical assumptions regarding the statistical nature of errors (e.g., mutual independence and orthogonality with respect to the truth) are required for TCA estimates to be unbiased. Even though soil moisture studies commonly acknowledge that these assumptions are required for an unbiased TCA, no study has specifically investigated the degree to which errors in existing soil moisture datasets conform to these assumptions. Here these assumptions are evaluated both analytically and numerically over four extensively instrumented watershed sites using soil moisture products derived from active microwave remote sensing, passive microwave remote sensing, and a land surface model. Results demonstrate that nonorthogonal and error cross-covariance terms represent a significant fraction of the total variance of these products. However, the overall impact of error cross correlation on TCA is found to be significantly larger than the impact of nonorthogonal errors. Because of the impact of cross-correlated errors, TCA error estimates generally underestimate the true random error of soil moisture products.


2011 ◽  
Vol 4 (4) ◽  
pp. 1115-1131 ◽  
Author(s):  
J. Mao ◽  
S. J. Phipps ◽  
A. J. Pitman ◽  
Y. P. Wang ◽  
G. Abramowitz ◽  
...  

Abstract. The CSIRO Mk3L climate system model, a reduced-resolution coupled general circulation model, has previously been described in this journal. The model is configured for millennium scale or multiple century scale simulations. This paper reports the impact of replacing the relatively simple land surface scheme that is the default parameterisation in Mk3L with a sophisticated land surface model that simulates the terrestrial energy, water and carbon balance in a physically and biologically consistent way. An evaluation of the new model's near-surface climatology highlights strengths and weaknesses, but overall the atmospheric variables, including the near-surface air temperature and precipitation, are simulated well. The impact of the more sophisticated land surface model on existing variables is relatively small, but generally positive. More significantly, the new land surface scheme allows an examination of surface carbon-related quantities including net primary productivity which adds significantly to the capacity of Mk3L. Overall, results demonstrate that this reduced-resolution climate model is a good foundation for exploring long time scale phenomena. The addition of the more sophisticated land surface model enables an exploration of important Earth System questions including land cover change and abrupt changes in terrestrial carbon storage.


2008 ◽  
Vol 9 (1) ◽  
pp. 116-131 ◽  
Author(s):  
Bart van den Hurk ◽  
Janneke Ettema ◽  
Pedro Viterbo

Abstract This study aims at stimulating the development of soil moisture data assimilation systems in a direction where they can provide both the necessary control of slow drift in operational NWP applications and support the physical insight in the performance of the land surface component. It addresses four topics concerning the systematic nature of soil moisture data assimilation experiments over Europe during the growing season of 2000 involving the European Centre for Medium-Range Weather Forecasts (ECMWF) model infrastructure. In the first topic the effect of the (spinup related) bias in 40-yr ECMWF Re-Analysis (ERA-40) precipitation on the data assimilation is analyzed. From results averaged over 36 European locations, it appears that about half of the soil moisture increments in the 2000 growing season are attributable to the precipitation bias. A second topic considers a new soil moisture data assimilation system, demonstrated in a coupled single-column model (SCM) setup, where precipitation and radiation are derived from observations instead of from atmospheric model fields. For many of the considered locations in this new system, the accumulated soil moisture increments still exceed the interannual variability estimated from a multiyear offline land surface model run. A third topic examines the soil water budget in response to these systematic increments. For a number of Mediterranean locations the increments successfully increase the surface evaporation, as is expected from the fact that atmospheric moisture deficit information is the key driver of soil moisture adjustment. In many other locations, however, evaporation is constrained by the experimental SCM setup and is hardly affected by the data assimilation. Instead, a major portion of the increments eventually leave the soil as runoff. In the fourth topic observed evaporation is used to evaluate the impact of the data assimilation on the forecast quality. In most cases, the difference between the control and data assimilation runs is considerably smaller than the (positive) difference between any of the simulations and the observations.


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