scholarly journals Online dynamical downscaling of temperature and precipitation within the <i>i</i>LOVECLIM model (version 1.1)

2018 ◽  
Vol 11 (1) ◽  
pp. 453-466
Author(s):  
Aurélien Quiquet ◽  
Didier M. Roche ◽  
Christophe Dumas ◽  
Didier Paillard
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Atmospheric Model ◽  
Scale Model ◽  
Model Parameters ◽  
Temperature And Precipitation ◽  
Large Scale Model

Abstract. This paper presents the inclusion of an online dynamical downscaling of temperature and precipitation within the model of intermediate complexity iLOVECLIM v1.1. We describe the following methodology to generate temperature and precipitation fields on a 40 km  ×  40 km Cartesian grid of the Northern Hemisphere from the T21 native atmospheric model grid. Our scheme is not grid specific and conserves energy and moisture in the same way as the original climate model. We show that we are able to generate a high-resolution field which presents a spatial variability in better agreement with the observations compared to the standard model. Although the large-scale model biases are not corrected, for selected model parameters, the downscaling can induce a better overall performance compared to the standard version on both the high-resolution grid and on the native grid. Foreseen applications of this new model feature include the improvement of ice sheet model coupling and high-resolution land surface models.

scholarly journals Online dynamical downscaling of temperature and precipitation within the iLOVECLIM model (version 1.1)

2017 ◽  
Author(s):  
Aurélien Quiquet ◽  
Didier M. Roche ◽  
Christophe Dumas ◽  
Didier Paillard
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Dynamical Downscaling ◽  
Land Surface Model ◽  
Atmospheric Model ◽  
Scale Model ◽  
Surface Model ◽  
Model Parameters ◽  
Temperature And Precipitation

Abstract. In this paper, we present the inclusion of an online dynamical downscaling of heat and moisture within the model of intermediate complexity iLOVECLIM v1.1. We describe the followed methodology to generate temperature and precipitation fields on a 40 km × 40 km Cartesian grid of the Northern Hemisphere from the T21 native atmospheric model grid. Our scheme is non grid-specific and conserves energy and moisture. We show that we are able to generate a high resolution field which presents a spatial variability in better agreement with the observations compared to the standard model. Whilst the large-scale model biases are not corrected, for selected model parameters, the downscaling can induce a better overall performance compared to the standard version on both the high-resolution grid and on the native grid. Foreseen applications of this new model feature includes ice sheet model coupling and high-resolution land surface model.

A High-Resolution Climate Model for the U.S. Pacific Northwest: Mesoscale Feedbacks and Local Responses to Climate Change*

2008 ◽  
Vol 21 (21) ◽  
pp. 5708-5726 ◽  
Author(s):  
Eric P. Salathé ◽  
Richard Steed ◽  
Clifford F. Mass ◽  
Patrick H. Zahn
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Snow Cover ◽  
Pacific Northwest ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Model ◽  
Mesoscale Model ◽  
Global Model ◽  
Temperature And Precipitation

Abstract Simulations of future climate scenarios produced with a high-resolution climate model show markedly different trends in temperature and precipitation over the Pacific Northwest than in the global model in which it is nested, apparently because of mesoscale processes not being resolved at coarse resolution. Present-day (1990–99) and future (2020–29, 2045–54, and 2090–99) conditions are simulated at high resolution (15-km grid spacing) using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) system and forced by ECHAM5 global simulations. Simulations use the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2 emissions scenario, which assumes a rapid increase in greenhouse gas concentrations. The mesoscale simulations produce regional alterations in snow cover, cloudiness, and circulation patterns associated with interactions between the large-scale climate change and the regional topography and land–water contrasts. These changes substantially alter the temperature and precipitation trends over the region relative to the global model result or statistical downscaling. Warming is significantly amplified through snow–albedo feedback in regions where snow cover is lost. Increased onshore flow in the spring reduces the daytime warming along the coast. Precipitation increases in autumn are amplified over topography because of changes in the large-scale circulation and its interaction with the terrain. The robustness of the modeling results is established through comparisons with the observed and simulated seasonal variability and with statistical downscaling results.

scholarly journals A High-Resolution Dataset of Water Fluxes and States for Germany Accounting for Parametric Uncertainty

2016 ◽  
Author(s):  
Matthias Zink ◽  
Rohini Kumar ◽  
Matthias Cuntz ◽  
Luis Samaniego
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Groundwater Recharge ◽  
Land Surface ◽  
Large Scale ◽  
Parametric Uncertainty ◽  
Model Parameters ◽  
High Resolution Data ◽  
Daily Discharge ◽  
Resolution Dataset

Abstract. Long term, high-resolution data about hydrologic fluxes and states are needed for many hydrological applications. Because continuous large-scale observations of such variables are not feasible, hydrologic or land surface models are applied to derive them. This study aims to analyze and provide a consistent high-resolution dataset of land surface variables over Germany, accounting for uncertainties caused by equifinal model parameters. The mesoscale Hydrological Model (mHM) is employed to derive an ensemble (100 members) of evapotranspiration, groundwater recharge, soil moisture and generated runoff at high spatial and temporal resolutions (4 km and daily, respectively) for the period 1951–2010. The model is cross-evaluated against the observed runoff in 222 catchments, which are not used for model calibration. The mean (standard deviation) of the ensemble median NSE estimated for these catchments is 0.68 (0.09) for daily discharge simulations. The modeled evapotranspiration and soil moisture reasonably represent the observations from eddy covariance stations. Our analysis indicates the lowest parametric uncertainty for evapotranspiration, and the largest is observed for groundwater recharge. The uncertainty of the hydrologic variables varies over the course of a year, with the exception of evapotranspiration, which remains almost constant. This study emphasizes the role of accounting for the parametric uncertainty in model-derived hydrological datasets.

scholarly journals Mini-batch optimization enables training of ODE models on large-scale datasets

2019 ◽  
Author(s):  
Paul Stapor ◽  
Leonard Schmiester ◽  
Christoph Wierling ◽  
Bodo M.H. Lange ◽  
Daniel Weindl ◽  
...  
Keyword(s):  
Large Scale ◽  
Optimization Methods ◽  
Scale Model ◽  
Model Parameters ◽  
Dynamical Models ◽  
Scaling Properties ◽  
Ode Models ◽  
Large Scale Model ◽  
Cellular Signal ◽  
Order Of Magnitude

AbstractQuantitative dynamical models are widely used to study cellular signal processing. A critical step in modeling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. However, mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark mini-batch optimization for ordinary differential equation (ODE) models thereby establishing a direct link between dynamic modeling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modeling of even larger and more complex systems than what is currently possible.

High Resolution WRF Regional Climate Modeling for the Andes-Amazon Transition Region: Model Validation Early Results

2020 ◽  
Author(s):  
Juan Pablo Sierra ◽  
Clementine Junquas ◽  
Jhan Carlo Epinoza ◽  
Thierry Lebel ◽  
Hans Segura
Keyword(s):  
High Resolution ◽  
Transition Region ◽  
Land Surface ◽  
Large Scale ◽  
Amazon Basin ◽  
Climate Model ◽  
Rainfall Variability ◽  
Regional Climate ◽  
The Andes ◽  
Early Results

&lt;p&gt;&lt;span&gt;The western Amazon and eastern flank of the Andes form what is known as the Amazon-Andes transition region. This region is characterized by the presence of the rainiest area in the Amazon basin with an average precipitation ranging from 6000 to 7000 mm per year. This rainy zone is the result of interactions between large-scale circulation and local features. However, the physical mechanisms controlling this rainfall patterns in the transition region are poorly understood. On the other hand, high precipitation values in the area, along with erosion, sediment transport and the geological mountain uplift help to explain this region as one of the most species-rich terrestrial ecosystems. Nevertheless, accelerated deforestation rates reported both in tropical Andes and central-southern Amazon threat the biodiversity hotspots and can induce alterations in land surface energy and water balances. In this context, the use of regional climate models can shed light on the possible consequences of deforestation on rainfall in the transition region. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;The early results presented here are the first step in a work that seeks to gain a better understanding in the mechanisms involved in precipitation generation over the Amazon-Andes transition region, as well as the assessment of deforestation impacts on spatial and temporal rainfall variability during austral summer. The Weather Research and Forecasting (WRF) regional climate model is used with three nested domains. High resolution simulations (1km horizontal grid size) are performed over the key regions of Cuzco and Bolivian slopes. As a perspective,&lt;span&gt;&amp;#160; &lt;/span&gt;deforestation scenarios following the land use change trajectory observed during the last decade will be used in future works. The results of this work can help to dimension the consequences of deforestation on key ecosystems such as Andean hotspots.&lt;/span&gt;&lt;/p&gt;

scholarly journals Streamflow Data from Small Basins: A Challenging Test to High-Resolution Regional Climate Modeling

2011 ◽  
Vol 12 (5) ◽  
pp. 900-912 ◽  
Author(s):  
Kerstin Stahl ◽  
Lena M. Tallaksen ◽  
Lukas Gudmundsson ◽  
Jens H. Christensen
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Temporal Dynamics ◽  
Model Simulation ◽  
Regional Climate ◽  
Regional Scale ◽  
Climate Simulation ◽  
Change Scenario

Abstract Land surface models and large-scale hydrological models provide the basis for studying impacts of climate and anthropogenic changes on continental- to regional-scale hydrology. Hence, there is a need for comparison and validation of simulated characteristics of spatial and temporal dynamics with independent observations. This study introduces a novel validation framework that relates to common hydrological design measures. The framework is tested by comparing anomalies of runoff from a high-resolution climate-model simulation for Europe with a large number of streamflow observations from small near-natural basins. The regional climate simulation was performed as a “poor man’s reanalysis,” involving a dynamical downscaling of the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) with the Danish “HIRHAM5” model. For 19 different anomaly levels, two indices evaluate the temporal agreement (i.e., the occurrence and frequency of dry and wet events based on daily anomalies), whereas two other indices compare the interannual variability and trends based on annual anomalies. Benchmarks on each index facilitated a comparison across indices, anomaly levels, and basins. The lowest agreement of observed and simulated anomalies was found for dry anomalies. Weak to moderately wet anomalies agreed best, but agreement dropped again for the wettest anomalies. The results could guide the decision on thresholds if this regional climate model were used for the assessment of climate change scenario impacts on flood and drought statistics. Indices vary across Europe, but a gradient with decreasing correspondence between observed and simulated runoff characteristics from west to east, from lower to higher elevations, and from fast to slowly responding basins can be distinguished. The suggested indices can easily be adapted to other study areas and model types to assist in assessing the reliability of predictions of hydrological change.

scholarly journals Improving Global Model Precipitation Forecasts over India Using Downscaling and the FSU Superensemble. Part II: Seasonal Climate

2009 ◽  
Vol 137 (9) ◽  
pp. 2736-2757 ◽  
Author(s):  
Arindam Chakraborty ◽  
T. N. Krishnamurti
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Skill Score ◽  
Rain Gauge ◽  
Scale Model ◽  
Seasonal Forecasts ◽  
Coupled Models ◽  
Large Scale Model ◽  
Regional Downscaling ◽  
Seasonal Time Scale

Abstract This study addresses seasonal forecasts of rains over India using the following components: high-resolution rain gauge–based rainfall data covering the years 1987–2001, rain-rate initialization, four global atmosphere–ocean coupled models, a regional downscaling of the multimodel forecasts, and a multimodel superensemble that includes a training and a forecast phase at the high resolution over the internal India domain. The results of monthly and seasonal forecasts of rains for the member models and for the superensemble are presented here. The main findings, assessed via the use of RMS error, anomaly correlation, equitable threat score, and ranked probability skill score, are (i) high forecast skills for the downscaled superensemble-based seasonal forecasts compared to the forecasts from the direct use of large-scale model forecasts were possible; (ii) very high scores for rainfall forecasts have been noted separately for dry and wet years, for different regions over India and especially for heavier rains in excess of 15 mm day−1; and (iii) the superensemble forecast skills exceed that of the benchmark observed climatology. The availability of reliable measures of high-resolution rain gauge–based rainfall was central for this study. Overall, the proposed algorithms, added together, show very promising results for the prediction of monsoon rains on the seasonal time scale.

scholarly journals A high-resolution dataset of water fluxes and states for Germany accounting for parametric uncertainty

2017 ◽  
Vol 21 (3) ◽  
pp. 1769-1790 ◽  
Author(s):  
Matthias Zink ◽  
Rohini Kumar ◽  
Matthias Cuntz ◽  
Luis Samaniego
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Groundwater Recharge ◽  
Land Surface ◽  
Large Scale ◽  
Parametric Uncertainty ◽  
Model Parameters ◽  
High Resolution Data ◽  
Daily Streamflow ◽  
Resolution Dataset

Abstract. Long-term, high-resolution data about hydrologic fluxes and states are needed for many hydrological applications. Because continuous large-scale observations of such variables are not feasible, hydrologic or land surface models are applied to derive them. This study aims to analyze and provide a consistent high-resolution dataset of land surface variables over Germany, accounting for uncertainties caused by equifinal model parameters. The mesoscale Hydrological Model (mHM) is employed to derive an ensemble (100 members) of evapotranspiration, groundwater recharge, soil moisture, and runoff generated at high spatial and temporal resolutions (4 km and daily, respectively) for the period 1951–2010. The model is cross-evaluated against the observed daily streamflow in 222 basins, which are not used for model calibration. The mean (standard deviation) of the ensemble median Nash–Sutcliffe efficiency estimated for these basins is 0.68 (0.09) for daily streamflow simulations. The modeled evapotranspiration and soil moisture reasonably represent the observations from eddy covariance stations. Our analysis indicates the lowest parametric uncertainty for evapotranspiration, and the largest is observed for groundwater recharge. The uncertainty of the hydrologic variables varies over the course of a year, with the exception of evapotranspiration, which remains almost constant. This study emphasizes the role of accounting for the parametric uncertainty in model-derived hydrological datasets.

scholarly journals IBI-CCS: a regional high-resolution model to evaluate western Europe sea level changes

2021 ◽  
Author(s):  
Alisée A. Chaigneau ◽  
Guillaume Reffray ◽  
Aurore Voldoire ◽  
Angélique Melet
Keyword(s):  
High Resolution ◽  
Sea Level ◽  
General Circulation ◽  
Large Scale ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Ocean Model ◽  
Global Climate Models ◽  
Climate Change Scenarios ◽  
Complete Representation

Abstract. Projections of coastal sea level (SL) changes are of great interest for coastal risk assessment and decision-making. SL projections are typically produced using global climate models (GCMs) which cannot fully resolve SL changes at the coast due to their coarse resolution and lack of representation of some relevant processes. To overcome these limitations and refine projections at regional scales, GCMs can be dynamically downscaled through the implementation of a high-resolution regional climate model (RCM). In this study, we developed the IBI-CCS regional ocean model based on a 1/12 ° north-eastern Atlantic NEMO ocean model configuration to dynamically downscale CNRM-CM6-1-HR, a GCM with a ¼ ° resolution ocean model component developed for the Coupled Model Intercomparison Project 6th Phase (CMIP6) by the Centre National de Recherches Météorologiques (CNRM). For a more complete representation of processes driving coastal SL changes, tides and atmospheric surface pressure forcing are explicitly resolved in IBI-CCS in addition to the ocean general circulation. To limit the propagation of climate drifts and biases from the GCM into the regional simulations, several corrections are applied to the GCM fields used to force the RCM. The regional simulations are performed over the 1950 to 2100 period for two climate change scenarios (SSP1-2.6 and SSP5-8.5). To validate the dynamical downscaling method, the RCM and GCM simulations are compared to reanalyses and observations over the 1993–2014 period for a selection of ocean variables including SL. Results indicate that large-scale performances of IBI-CCS are better than those of the GCM thanks to the corrections applied to the RCM. Extreme SLs are also satisfactorily represented in the IBI-CCS historical simulation. Comparison of the RCM and GCM 21st century projections show a limited impact of increased resolution (1/4° to 1/12°) on SL changes. Overall, bias corrections have a moderate impact on projected coastal SL changes projections, except in the Mediterranean Sea where GCM biases were substantial.

High resolution solar atlas for Greece under climate change

2020 ◽  
Author(s):  
Iason Markantonis ◽  
Nadia Politi ◽  
Diamando Vlachogiannis ◽  
Nikolaos Gounaris ◽  
Karozis Stylianos ◽  
...  
Keyword(s):  
High Resolution ◽  
Solar Energy ◽  
Large Scale ◽  
Climate Model ◽  
Sunshine Duration ◽  
Wrf Model ◽  
Atmospheric Model ◽  
Computational Time ◽  
The Future ◽  
Spatio Temporal

&lt;p&gt;Renewable energy (RE) is considered as the most attractive and climate friendly source of energy to mitigate GHG effects. Due to the inherent, &amp;#8220;non stationary&amp;#8221; nature of climate, it is of paramount importance to be able make risk-informed decisions considering also the future conditions when installing / operating RES in Greece. As the amount of solar energy falling on the earth&amp;#8217;s surface is highly influenced by local and large scale atmospheric movement conditions, high resolution simulations should be used to calculate it.&lt;/p&gt;&lt;p&gt;The aim of this research is to generate a climatology atlas of mean yearly and seasonal values for the GHI for the &amp;#8220;historic time period&amp;#8221; 1980-2009 and compare it against future values in 2020-2050. The current study employs high resolution downscaled climate model data to generate future solar radiation atlases for Greece based on RCP4.5 and RCP8.5 scenarios. Greece is a country with high potential in renewable solar energy. Several studies have mentioned the high amount of sunshine hours in most parts of the country, (e.g. Matzarakis &amp; Katsoulis, 2006&lt;sup&gt;1&lt;/sup&gt;, HNMS&lt;sup&gt;2&lt;/sup&gt;).&lt;/p&gt;&lt;p&gt;The data for both historic and future period analyses are produced from WRF 5km downscaled model output with temporal resolution of 6 hours, using as input ERA-INTERIM and EC-EARTH input data, respectively. The study that has produced the atmospheric model dataset is described in Politi, et al. (2018)&lt;sup&gt;3&lt;/sup&gt;. We explore spatio-temporal changes of the GHI climatology and identify those areas which will exhibit considerable changes in the future.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References:&lt;/strong&gt;&lt;/p&gt;&lt;ol&gt;&lt;li&gt;(Matzarakis &amp; Katsoulis, 2006), &amp;#8216;Sunshine duration hours over the Greek region&amp;#8217;. Theoritical and Applied Climatology.&lt;/li&gt; &lt;li&gt;HNMS, &amp;#8216;Greece Climate Atlas&amp;#8217;. http://climatlas.hnms.gr/sdi/&lt;/li&gt; &lt;li&gt;(Politi, N. et al. 2018) &amp;#8216;Evaluation of the AWR-WRF model configuration at high resolution over the domain of Greece&amp;#8217;. Atmospheric Research Volume 208, 1 August 2018, Pages 229-245.&lt;/li&gt; &lt;/ol&gt;&lt;p&gt;Acknowledgments&lt;br&gt;This work was supported by computational time granted from the Greek Research &amp; Technology Network (GRNET) in the National HPC facility &amp;#8211; ARIS - under project ID HRCOG (pr004020).&lt;/p&gt;

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