scholarly journals Microscale Numerical Prediction over Montreal with the Canadian External Urban Modeling System

2011 ◽  
Vol 50 (12) ◽  
pp. 2410-2428 ◽  
Author(s):  
Sylvie Leroyer ◽  
Stéphane Bélair ◽  
Jocelyn Mailhot ◽  
Ian B. Strachan
Keyword(s):  
Land Surface ◽  
Deterministic Model ◽  
Weather Prediction ◽  
Grid Size ◽  
Metropolitan Region ◽  
Balance Model ◽  
Surface Model ◽  
Limited Area ◽  
Computational Domain ◽  
Near Surface

AbstractThe Canadian urban and land surface external modeling system (known as urban GEM-SURF) has been developed to provide surface and near-surface meteorological variables to improve numerical weather prediction and to become a tool for environmental applications. The system is based on the Town Energy Balance model for the built-up covers and on the Interactions between the Surface, Biosphere, and Atmosphere land surface model for the natural covers. It is driven by coarse-resolution forecasts from the 15-km Canadian regional operational model. This new system was tested for a 120-m grid-size computational domain covering the Montreal metropolitan region from 1 May to 30 September 2008. The numerical results were first evaluated against local observations of the surface energy budgets, air temperature, and humidity taken at the Environmental Prediction in Canadian Cities (EPiCC) field experiment tower sites. As compared with the regional deterministic 15-km model, important improvements have been achieved with this system over urban and suburban sites. GEM-SURF’s ability to simulate the Montreal surface urban heat island was also investigated, and the radiative surface temperatures from this system and from two systems operational at the Meteorological Service of Canada were compared, that is, the 15-km regional deterministic model and the so-called limited-area model with 2.5-km grid size. Comparison of urban GEM-SURF outputs with remotely sensed observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) reveals relatively good agreement for urban and natural areas.

scholarly journals Climate pattern-scaling set for an ensemble of 22 GCMs – adding uncertainty to the IMOGEN version 2.0 impact system

2018 ◽  
Vol 11 (2) ◽  
pp. 541-560 ◽  
Author(s):  
Przemyslaw Zelazowski ◽  
Chris Huntingford ◽  
Lina M. Mercado ◽  
Nathalie Schaller
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Land Surface ◽  
Land Surface Model ◽  
Individual Performance ◽  
Balance Model ◽  
Surface Model ◽  
Near Surface ◽  
Global Circulation Models ◽  
Version 2.0

Abstract. Global circulation models (GCMs) are the best tool to understand climate change, as they attempt to represent all the important Earth system processes, including anthropogenic perturbation through fossil fuel burning. However, GCMs are computationally very expensive, which limits the number of simulations that can be made. Pattern scaling is an emulation technique that takes advantage of the fact that local and seasonal changes in surface climate are often approximately linear in the rate of warming over land and across the globe. This allows interpolation away from a limited number of available GCM simulations, to assess alternative future emissions scenarios. In this paper, we present a climate pattern-scaling set consisting of spatial climate change patterns along with parameters for an energy-balance model that calculates the amount of global warming. The set, available for download, is derived from 22 GCMs of the WCRP CMIP3 database, setting the basis for similar eventual pattern development for the CMIP5 and forthcoming CMIP6 ensemble. Critically, it extends the use of the IMOGEN (Integrated Model Of Global Effects of climatic aNomalies) framework to enable scanning across full uncertainty in GCMs for impact studies. Across models, the presented climate patterns represent consistent global mean trends, with a maximum of 4 (out of 22) GCMs exhibiting the opposite sign to the global trend per variable (relative humidity). The described new climate regimes are generally warmer, wetter (but with less snowfall), cloudier and windier, and have decreased relative humidity. Overall, when averaging individual performance across all variables, and without considering co-variance, the patterns explain one-third of regional change in decadal averages (mean percentage variance explained, PVE, 34.25±5.21), but the signal in some models exhibits much more linearity (e.g. MIROC3.2(hires): 41.53) than in others (GISS_ER: 22.67). The two most often considered variables, near-surface temperature and precipitation, have a PVE of 85.44±4.37 and 14.98±4.61, respectively. We also provide an example assessment of a terrestrial impact (changes in mean runoff) and compare projections by the IMOGEN system, which has one land surface model, against direct GCM outputs, which all have alternative representations of land functioning. The latter is noted as an additional source of uncertainty. Finally, current and potential future applications of the IMOGEN version 2.0 modelling system in the areas of ecosystem modelling and climate change impact assessment are presented and discussed.

scholarly journals High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation

2020 ◽  
Author(s):  
Benjamin Fersch ◽  
Alfonso Senatore ◽  
Bianca Adler ◽  
Joël Arnault ◽  
Matthias Mauder ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Land Surface ◽  
Hydrological Modeling ◽  
Weather Prediction ◽  
Surface Model ◽  
Near Surface ◽  
Ground Heat Flux ◽  
The Impact ◽  
Fully Coupled ◽  
Regional Water

<p>The land surface and the atmospheric boundary layer are closely intertwined with respect to the exchange of water, trace gases and energy. Nonlinear feedback and scale dependent mechanisms are obvious by observations and theories. Modeling instead is often narrowed to single compartments of the terrestrial system or bound to traditional viewpoints of definite scientific disciplines. Coupled terrestrial hydrometeorological modeling systems attempt to overcome these limitations to achieve a better integration of the processes relevant for regional climate studies and local area weather prediction. We examine the ability of the hydrologically enhanced version of the Weather Research and Forecasting Model (WRF-Hydro) to reproduce the regional water cycle by means of a two-way coupled approach and assess the impact of hydrological coupling with respect to a traditional regional atmospheric model setting. It includes the observation-based calibration of the hydrological model component (offline WRF-Hydro) and a comparison of the classic WRF and the fully coupled WRF-Hydro models both with identical calibrated parameter settings for the land surface model (Noah-MP). The simulations are evaluated based on extensive observations at the pre-Alpine Terrestrial Environmental Observatory (TERENO Pre-Alpine) for the Ammer (600 km²) and Rott (55 km²) river catchments in southern Germany, covering a five month period (Jun–Oct 2016).</p><p>The sensitivity of 7 land surface parameters is tested using the <em>Latin-Hypercube One-factor-At-a-Time</em> (LH-OAT) method and 6 sensitive parameters are subsequently optimized for 6 different subcatchments, using the Model-Independent <em>Parameter Estimation and Uncertainty Analysis software</em> (PEST).</p><p>The calibration of the offline WRF-Hydro leads to Nash-Sutcliffe efficiencies between 0.56 and 0.64 and volumetric efficiencies between 0.46 and 0.81 for the six subcatchments. The comparison of classic WRF and fully coupled WRF-Hydro shows only tiny alterations for radiation and precipitation but considerable changes for moisture- and energy fluxes. By comparison with TERENO Pre-Alpine observations, the fully coupled model slightly outperforms the classic WRF with respect to evapotranspiration, sensible and ground heat flux, near surface mixing ratio, temperature, and boundary layer profiles of air temperature. The subcatchment-based water budgets show uniformly directed variations for evapotranspiration, infiltration excess and percolation whereas soil moisture and precipitation change randomly.</p>

scholarly journals ERA5-Land: A state-of-the-art global reanalysis dataset for land applications

2021 ◽  
Author(s):  
Joaquín Muñoz-Sabater ◽  
Emanuel Dutra ◽  
Anna Agustí-Panareda ◽  
Clément Albergel ◽  
Gabriele Arduini ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Land Surface ◽  
Climate Models ◽  
Hydrological Cycle ◽  
State Of The Art ◽  
Weather Prediction ◽  
Added Value ◽  
Surface Model ◽  
Climate Data ◽  
Near Surface

Abstract. Framed within the Copernicus Climate Change Service of the European Commission, the European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the 5th generation of European ReAnalysis (ERA5), hereafter named as ERA5-Land. Once completed, the period covered will span from 1950 to present, with continuous updates to support land monitoring applications. ERA5-Land describes the evolution of the water and energy cycles over land in a consistent manner over the production period, enabling the characterisation of trends and anomalies. This is achieved through global high resolution numerical integrations of the ECMWF land surface model driven by the downscaled meteorological forcing from the ERA5 climate reanalysis, including an elevation correction for the thermodynamic near-surface state. ERA5-Land shares with ERA5 most of the parametrizations that guarantees the use of the state-of-the-art land surface modeling applied to Numerical Weather Prediction (NWP) models. A main advantage of ERA5-Land compared to ERA5 and the older ERA-Interim is the horizontal resolution, which is enhanced globally to 9 km compared to 31 km (ERA5) or 80 km (ERA-Interim), whereas the temporal resolution is hourly as in ERA5. Evaluation against independent in situ observations and global model or satellite-based reference datasets shows the added value of ERA5-Land in the description of the hydrological cycle, in particular with enhanced soil moisture and lake description, and an overall better agreement of river discharge estimations with available observations. However, ERA5-Land snow depth fields present a mixed behaviour when compared to those of ERA5, depending on geographical location and altitude. The description of the energy cycle shows comparable results with ERA5. Nevertheless, ERA5-Land reduces the global averaged root mean square error of the skin temperature, taking as reference MODIS data, mainly due to the contribution of coastal points where spatial resolution is important. Since January 2020, the ERA5-Land period available extends from January 1981 to near present, with 2 to 3 months delay with respect to real-time. The segment prior to 1981 is in production, aiming to a release of the whole dataset in summer 2021. The high spatial and temporal resolution of ERA5-Land, its extended period, and the consistency of the fields produced makes it a valuable dataset to support hydrological studies, to initialise NWP and climate models, and to support diverse applications dealing with water resource, land and environmental management. The full ERA5-Land hourly and monthly averaged dataset presented in this paper are available through the Climate Data Store, https://doi.org/10.24381/cds.e2161bac and https://doi.org/10.24381/cds.68d2bb30, respectively.

scholarly journals High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation

2019 ◽  
Author(s):  
Benjamin Fersch ◽  
Alfonso Senatore ◽  
Bianca Adler ◽  
Joël Arnault ◽  
Matthias Mauder ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Land Surface ◽  
Hydrological Modeling ◽  
Weather Prediction ◽  
Surface Model ◽  
Near Surface ◽  
Ground Heat Flux ◽  
The Impact ◽  
Fully Coupled ◽  
Regional Water

Abstract. The land surface and the atmospheric boundary layer are closely intertwined with respect to the exchange of water, trace gases and energy. Nonlinear feedback and scale dependent mechanisms are obvious by observations and theories. Modeling instead is often narrowed to single compartments of the terrestrial system or largely bound to traditional disciplines. Coupled terrestrial hydrometeorological modeling systems attempt to overcome these limitations to achieve a better integration of the processes relevant for regional climate studies and local area weather prediction. This study examines the ability of the hydrologically enhanced version of the Weather Research and Forecasting Model (WRF-Hydro) to reproduce the regional water cycle by means of a two-way coupled approach and assesses the impact of hydrological coupling with respect to a traditional regional atmospheric model setting. It includes the observation-based calibration of the hydrological model component (offline WRF-Hydro) and a comparison of the classic WRF and the fully coupled WRF-Hydro models both with identical calibrated parameter settings for the land surface model (Noah-MP). The simulations are evaluated based on extensive observations at the preAlpine Terrestrial Environmental Observatory (TERENO-preAlpine) for the Ammer (600 km2) and Rott (55 km2) river catchments in southern Germany, covering a five month period (Jun–Oct 2016). The sensitivity of 7 land surface parameters is tested using the Latin-Hypercube One-factor-At-a-Time (LH-OAT) method and 6 sensitive parameters are subsequently optimized for 6 different subcatchments, using the Model-Independent Parameter Estimation and Uncertainty Analysis software (PEST). The calibration of the offline WRF-Hydro gives Nash-Sutcliffe efficiencies between 0.56 and 0.64 and volumetric efficiencies between 0.46 and 0.81 for the six subcatchments. The comparison of classic WRF and fully coupled WRF-Hydro, both using the calibrated parameters from the offline model, shows nominal alterations for radiation and precipitation but considerable changes for moisture- and heat fluxes. By comparison with TERENO-preAlpine observations, the fully coupled model slightly outperforms the classic WRF with respect to evapotranspiration, sensible and ground heat flux, near surface mixing ratio, temperature, and boundary layer profiles of air temperature. The subcatchment-based water budgets show uniformly directed variations for evapotranspiration, infiltration excess and percolation whereas soil moisture and precipitation change randomly.

scholarly journals The role of soil states in medium-range weather predictability

2001 ◽  
Vol 8 (6) ◽  
pp. 373-386 ◽  
Author(s):  
S. Fukutome ◽  
C. Prim ◽  
C. Schär
Keyword(s):  
Land Surface ◽  
Weather Prediction ◽  
Land Surface Model ◽  
Soil Conditions ◽  
Surface Model ◽  
Limited Area ◽  
Coupled Modes ◽  
Unstable Phase ◽  
Medium Range

Abstract. Current day operational ensemble weather prediction systems generally rely upon perturbed atmospheric initial states, thereby neglecting the eventual effect on the atmospheric evolution that uncertainties in initial soil temperature and moisture fields could bring about during the summer months. The purpose of this study is to examine the role of the soil states in medium-range weather predictability. A limited area weather prediction model is used with the atmosphere/ land-surface system in coupled or uncoupled mode. It covers Europe and part of the north Atlantic, and is driven by prescribed sea-surface temperatures over the sea, and by atmospheric reanalyses at its lateral boundaries. A series of  3 member ensembles of summer simulations are used to assess the predictability of a reference simulation assumed to be perfect. In a first step, two ensembles are simulated: the first with the atmosphere coupled to the land-surface model, the second in the uncoupled mode with perfect soil conditions prescribed every 6 hours. Subsequent experiments are combinations thereof, in which the uncoupled and coupled modes alternate in the course of a simulation. The results show that there are "stable" and "unstable" periods in the weather evolution under consideration. During the stable periods, the predictability (measured in terms of ensemble spread at 500 hPa) of the coupled and uncoupled dynamical systems is almost identical; prescribing the perfect soil conditions has a negligible impact upon the atmospheric predictability. In contrast, the predictability during an unstable phase is found to be remarkably improved in the uncoupled ensembles. This effect results from guiding the atmospheric phase-space trajectory along its perfect evolution. It persists even when switching back from the uncoupled to the coupled mode prior to the onset of the unstable phase, a result that underlines the importance of soil moisture and temperature in data assimilation systems.

scholarly journals High-resolution fully coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation

2020 ◽  
Vol 24 (5) ◽  
pp. 2457-2481 ◽  
Author(s):  
Benjamin Fersch ◽  
Alfonso Senatore ◽  
Bianca Adler ◽  
Joël Arnault ◽  
Matthias Mauder ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Land Surface ◽  
Hydrological Modeling ◽  
Weather Prediction ◽  
Surface Model ◽  
Near Surface ◽  
Ground Heat Flux ◽  
The Impact ◽  
Fully Coupled ◽  
Regional Water

Abstract. The land surface and the atmospheric boundary layer are closely intertwined with respect to the exchange of water, trace gases, and energy. Nonlinear feedback and scale-dependent mechanisms are obvious by observations and theories. Modeling instead is often narrowed to single compartments of the terrestrial system or bound to traditional viewpoints of definite scientific disciplines. Coupled terrestrial hydrometeorological modeling systems attempt to overcome these limitations to achieve a better integration of the processes relevant for regional climate studies and local-area weather prediction. This study examines the ability of the hydrologically enhanced version of the Weather Research and Forecasting model (WRF-Hydro) to reproduce the regional water cycle by means of a two-way coupled approach and assesses the impact of hydrological coupling with respect to a traditional regional atmospheric model setting. It includes the observation-based calibration of the hydrological model component (offline WRF-Hydro) and a comparison of the classic WRF and the fully coupled WRF-Hydro models both with identically calibrated parameter settings for the land surface model (Noah-Multiparametrization; Noah-MP). The simulations are evaluated based on extensive observations at the Terrestrial Environmental Observatories (TERENO) Pre-Alpine Observatory for the Ammer (600 km2) and Rott (55 km2) river catchments in southern Germany, covering a 5-month period (June–October 2016). The sensitivity of seven land surface parameters is tested using the Latin-Hypercube–One-factor-At-a-Time (LH-OAT) method, and six sensitive parameters are subsequently optimized for six different subcatchments, using the model-independent Parameter Estimation and Uncertainty Analysis software (PEST). The calibration of the offline WRF-Hydro gives Nash–Sutcliffe efficiencies between 0.56 and 0.64 and volumetric efficiencies between 0.46 and 0.81 for the six subcatchments. The comparison of the classic WRF and fully coupled WRF-Hydro models, both using the calibrated parameters from the offline model, shows only tiny alterations for radiation and precipitation but considerable changes for moisture and heat fluxes. By comparison with TERENO Pre-Alpine Observatory measurements, the fully coupled model slightly outperforms the classic WRF model with respect to evapotranspiration, sensible and ground heat flux, the near-surface mixing ratio, temperature, and boundary layer profiles of air temperature. The subcatchment-based water budgets show uniformly directed variations for evapotranspiration, infiltration excess and percolation, whereas soil moisture and precipitation change randomly.

scholarly journals ERA5-Land: a state-of-the-art global reanalysis dataset for land applications

2021 ◽  
Vol 13 (9) ◽  
pp. 4349-4383
Author(s):  
Joaquín Muñoz-Sabater ◽  
Emanuel Dutra ◽  
Anna Agustí-Panareda ◽  
Clément Albergel ◽  
Gabriele Arduini ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Land Surface ◽  
Climate Models ◽  
Hydrological Cycle ◽  
State Of The Art ◽  
Weather Prediction ◽  
Added Value ◽  
Surface Model ◽  
Climate Data ◽  
Near Surface

Abstract. Framed within the Copernicus Climate Change Service (C3S) of the European Commission, the European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the fifth generation of European ReAnalysis (ERA5), hereafter referred to as ERA5-Land. Once completed, the period covered will span from 1950 to the present, with continuous updates to support land monitoring applications. ERA5-Land describes the evolution of the water and energy cycles over land in a consistent manner over the production period, which, among others, could be used to analyse trends and anomalies. This is achieved through global high-resolution numerical integrations of the ECMWF land surface model driven by the downscaled meteorological forcing from the ERA5 climate reanalysis, including an elevation correction for the thermodynamic near-surface state. ERA5-Land shares with ERA5 most of the parameterizations that guarantees the use of the state-of-the-art land surface modelling applied to numerical weather prediction (NWP) models. A main advantage of ERA5-Land compared to ERA5 and the older ERA-Interim is the horizontal resolution, which is enhanced globally to 9 km compared to 31 km (ERA5) or 80 km (ERA-Interim), whereas the temporal resolution is hourly as in ERA5. Evaluation against independent in situ observations and global model or satellite-based reference datasets shows the added value of ERA5-Land in the description of the hydrological cycle, in particular with enhanced soil moisture and lake description, and an overall better agreement of river discharge estimations with available observations. However, ERA5-Land snow depth fields present a mixed performance when compared to those of ERA5, depending on geographical location and altitude. The description of the energy cycle shows comparable results with ERA5. Nevertheless, ERA5-Land reduces the global averaged root mean square error of the skin temperature, taking as reference MODIS data, mainly due to the contribution of coastal points where spatial resolution is important. Since January 2020, the ERA5-Land period available has extended from January 1981 to the near present, with a 2- to 3-month delay with respect to real time. The segment prior to 1981 is in production, aiming for a release of the whole dataset in summer/autumn 2021. The high spatial and temporal resolution of ERA5-Land, its extended period, and the consistency of the fields produced makes it a valuable dataset to support hydrological studies, to initialize NWP and climate models, and to support diverse applications dealing with water resource, land, and environmental management. The full ERA5-Land hourly (Muñoz-Sabater, 2019a) and monthly (Muñoz-Sabater, 2019b) averaged datasets presented in this paper are available through the C3S Climate Data Store at https://doi.org/10.24381/cds.e2161bac and https://doi.org/10.24381/cds.68d2bb30, respectively.

Development of stochastically perturbed parameterization scheme for the Noah Land Surface Model with the optimized random forcing parameters using the micro-genetic algorithm

2021 ◽  
Author(s):  
Sujeong Lim ◽  
Claudio Cassardo ◽  
Seon Ki Park
Keyword(s):  
Genetic Algorithm ◽  
Land Surface ◽  
Weather Forecasting ◽  
Land Surface Model ◽  
Surface Model ◽  
Ensemble Spread ◽  
Near Surface ◽  
Random Forcing ◽  
Micro Genetic Algorithm ◽  
Stochastically Perturbed

<p>The ensemble data assimilation system is beneficial to represent the initial uncertainties and flow-dependent background error covariance (BEC). In particular, the inevitable model uncertainties can be expressed by ensemble spread, that is the standard deviation of ensemble BEC. However, the ensemble spread generally suffers from under-estimated problems. To alleviate this problem, recent studies employed stochastic perturbation schemes to increases the ensemble spreads by adding the random forcing in the model tendencies (i.e., physical or dynamical tendencies) or parameterization schemes (i.e., PBL, convective scheme, etc.). In this study, we focus on the near-surface uncertainties which are affected by the interactions between the land and atmosphere process. The land surface model (LSM) provides various fluxes as the lower boundary condition to the atmosphere, influencing the accuracy of hourly-to-seasonal scale weather forecasting, but the surface uncertainties were not much addressed yet. In this study, we developed the stochastically perturbed parameterization (SPP) scheme for the Noah LSM. The Weather Research and Forecasting (WRF) ensemble system is used for regional weather forecasting over East Asia, especially over the Korean Peninsula. As a testbed experiment with the newly-developed Noah LSM-SPP system, we first perturbed the soil temperature — a crucial variable for the near-surface forecasts by affecting sensible heat fluxes, land surface skin temperature and surface air temperature, and hence lower-tropospheric temperature. Here, the random forcing used in perturbation is made by the tuning parameters for amplitude, length scale, and time scales: they are commonly determined empirically by trial and error. In order to find optimal tuning parameter values, we applied a global optimization algorithm — the micro-genetic algorithm (micro-GA) — to achieve the smallest root-mean-squared errors. Our results indicate that optimization of the random forcing parameters contributes to an increase in the ensemble spread and a decrease in the ensemble mean errors in the near-surface and lower-troposphere uncertainties. Further experiments will be conducted by including soil moisture in the testbed.</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.

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.

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