Determining the range for CMIP climate projections for Europe using a sub-selected model ensemble based on key model performance indicators and key regional physical processes.

2020 ◽  
Author(s):  
Tamzin Palmer ◽  
Carol Mc Sweeney ◽  
Ben Booth
Keyword(s):  
Surface Temperature ◽  
Performance Indicators ◽  
Model Performance ◽  
Cmip5 Models ◽  
Climate Projections ◽  
Physical Processes ◽  
Near Surface ◽  
European Area ◽  
The Uk ◽  
The Impact

<p>An alternative approach to constraining climate projections based on a probabilistic approach with observational constraints, is to select a subset of models from the ensemble based on their ability to represent key physical processes, along with some indicators of model performance. The method that is presented here is based on the assumption that if a model is unable to reproduce the key factors important for determining the regional climate, the projections from this model are not considered reliable. The projection range for CMIP5 for the three EUCP European regions is assessed using two different subsampled model ensembles.</p><p>The first sub-sampling method presented uses the approach of Mc Sweeney et al. (2015), which assessed the models based on their performance for the UK climate. Each model in the CMIP5 ensemble (where data is available), is firstly assessed against these key performance indicators and poor performers eliminated from the selection. Several models also share large portions of code and therefore have similar errors and projections, Sanderson et al 2015a and 2015b quantifies these similarities. This analysis was used identify ‘near-neighbours’ and further reduce the selection. The applicability of a sub-selection of models based on their performance for the UK climate is assessed for the wider European area and found to reduce the projected range for the Northern European Area (NEU), for precipitation and near surface temperature considerably. The impact on the projected ranges for the Central European Area (CEU) and the Mediterranean (MED) was not as large, suggesting that a different set of physical processes are of primary importance for these regions.</p><p>To further investigate the effect of subsampling based on physical processes, a subset of CMIP5 models identified by the approach of Vogel et al. (2018) has been applied for the EUCP European areas. Vogel et al. (2018) looked at the ability of the CMIP5 models to reproduce the correlation between the hottest day of the year and precipitation within the same range as that found in the observations. This approach is designed to subsample the ensemble based on the ability of the model to represent soil moisture feedback processes with the atmosphere. It is thought that these processes are likely to be increasingly important for determining the projected climate in the CEU and MED regions.  </p><p>Finally, the projection range for the CMIP6 ensemble in the EUCP regions for precipitation and the near surface temperature will be presented and compared with those for CMIP5.</p>

Climate projections in fine resolution downscaling over South America: trends and classification of cyclonic systems

2020 ◽  
Author(s):  
Rosmeri Porfírio da Rocha ◽  
Michelle Simões Reboita ◽  
Natália Machado Crespo ◽  
Eduardo Marcos de Jesus ◽  
Andressa Andrade Cardoso ◽  
...  
Keyword(s):  
South America ◽  
Atlantic Ocean ◽  
South Atlantic ◽  
South Atlantic Ocean ◽  
Eastern Coast ◽  
Cmip5 Models ◽  
Climate Projections ◽  
Near Surface ◽  
Fine Resolution ◽  
The Impact

<p>Cyclones developing in eastern coast of South America impact weather and control the climate in most parts of the continent as well as over the South Atlantic Ocean. Current knowledge of these cyclones shows that they can have different thermal and dynamic structures along their lifecycles being classified as tropical, subtropical or extratropical. Cyclones occurring over the sea generate intense near-surface winds with major impacts on human activities and ecosystems. Given this context, we are producing fine resolution (~25 km) dynamic downscaling with RegCM4 to investigate the climatic trends of the different phases of cyclones over the southwest South Atlantic Ocean. Special emphasis will be given on the contribution of subtropical cyclones causing extreme events (rainfall and wind) in eastern Brazil. The simulations cover South America and wider area of South Atlantic Ocean. For evaluation simulation RegCM4 is forced by ERA-Interim reanalysis, while for the projections by CMIP5 models under RCP4.5 and RCP8.5 scenarios. Cyclones are tracked using an algorithm based on cyclonic relative vorticity. In this study we present the climatology of all cyclones provided by the ERA-Interim evaluation simulation in the period 1979-2015. Basically, we discuss the ability of fine resolution simulation in reproducing the main cyclogenetic areas over the continent, seasonality and interannual variability of cyclones. Comparisons with previous simulations allow discussing the impact of fine resolution downscaling on the climatological features of all cyclones and their classification in South America domain.    </p>

scholarly journals Evaluation of soil moisture in CMIP5 simulations over contiguous United States using in situ and satellite observations

2016 ◽  
Author(s):  
Shanshui Yuan ◽  
Steven M. Quiring
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Soil Column ◽  
Regional Analysis ◽  
Soil Layer ◽  
Model Performance ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Near Surface

Abstract. This study provides a comprehensive evaluation of soil moisture simulations in the Coupled Model Intercomparison Project Phase 5 (CMIP5) extended historical experiment (2003 to 2012). Soil moisture from in situ and satellite sources are used to evaluate CMIP5 simulations in the contiguous United States (CONUS). Both near-surface (0–10 cm) and soil column (0–100 cm) simulations from more than 14 CMIP5 models are evaluated during the warm season (April–September). Multi-model ensemble means and the performance of individual models are assessed at a monthly time scale. Our results indicate that CMIP5 models can reproduce the seasonal variability in soil moisture over CONUS. However, the models tend to overestimate the magnitude of both near-surface and soil-column soil moisture in the western U.S. and underestimate it in the eastern U.S. There are large variations in model performance, especially in the near-surface. There are significant regional and inter-model variations in performance. Results of a regional analysis show that in deeper soil layer, the CMIP5 soil moisture simulations tend to be most skillful in the southern U.S. Based on both the satellite-derived and in situ soil moisture, CESM1, CCSM4 and GFDL-ESM2M perform best in the 0–10 cm soil layer and CESM1, CCSM4, GFDL-ESM2M and HadGEM2-ES perform best in the 0–100 cm soil layer.

scholarly journals Regional responses of surface ozone in Europe to the location of high-latitude blocks and subtropical ridges

2016 ◽  
Author(s):  
Carlos Ordóñez ◽  
David Barriopedro ◽  
Ricardo García-Herrera ◽  
Pedro M. Sousa ◽  
Jordan L. Schnell
Keyword(s):  
High Latitude ◽  
Climate Models ◽  
Surface Ozone ◽  
Zonal Flow ◽  
Near Surface ◽  
Air Masses ◽  
Anticyclonic Circulation ◽  
The Uk ◽  
The Impact ◽  
First Time

Abstract. This paper analyses for the first time the impact of high-latitude blocks and subtropical ridges on near-surface ozone in Europe during a 15-year period. For this purpose, a catalogue of blocks and ridges over the Euro-Atlantic region is used together with a gridded dataset of maximum daily 8-hour running average ozone (MDA8 O3) covering the period 1998–2012. The response of ozone to the location of blocks and ridges with centres in three longitudinal sectors (Atlantic, ATL, 30º–0º W; European, EUR, 0º–30º E; Russian, RUS, 30º–60º E) is examined. The impact of blocks on ozone is regionally and seasonally dependent. In particular, blocks within the EUR sector yield positive ozone anomalies of ~ 5–10 ppb over large parts of central Europe in spring and northern Europe in summer. Over 20 % and 30 % of the days with blocks in that sector register exceedances of the 90th percentile of the seasonal ozone distribution at many European locations during spring and summer, respectively. The impacts of ridges during those seasons are subtle and more sensitive to their specific location, although they can trigger ozone anomalies of ~ 5–10 ppb in Italy and the surrounding countries in summer, eventually exceeding European air quality targets. During winter, surface ozone in the northwest of Europe presents completely opposite responses to blocks and ridges. The anticyclonic circulation associated with winter EUR blocking, and to a lesser extent with ATL blocking, yields negative ozone anomalies between −5 ppb and −10 ppb over the UK, Northern France and the Benelux. Conversely, the enhanced zonal flow around 50˚–60˚ N during the occurrence of ATL ridges favours the arrival of background air masses from the Atlantic and the ventilation of the boundary layer, producing positive ozone anomalies above 5 ppb in an area spanning from the British Isles to Germany. This work provides the first quantitative assessments of the remarkable but distinct impacts that the anticyclonic circulation and the diversion of the zonal flow associated with blocks and ridges exert on surface ozone in Europe. The findings reported here can be exploited in the future to evaluate the modelled responses of ozone to circulation changes within chemical transport models (CTMs) and chemistry-climate models (CCMs).

scholarly journals Implementing and measuring person-centredness using an APP for knowledge transfer: the iMPAKT app

2020 ◽  
Vol 32 (4) ◽  
pp. 251-258 ◽  
Author(s):  
Tanya McCance ◽  
Brighide M Lynch ◽  
Christine Boomer ◽  
Donna Brown ◽  
Christopher Nugent ◽  
...  
Keyword(s):  
Knowledge Transfer ◽  
Patient Experience ◽  
Performance Indicators ◽  
Key Performance Indicators ◽  
Structured Interviews ◽  
Evaluation Approach ◽  
Experience Of Care ◽  
Measurement Framework ◽  
The Uk ◽  
The Impact

Abstract Objective The aim of the study was to evaluate a technological solution in the form of an App to implement and measure person-centredness in nursing. The focus was to enhance the knowledge transfer of a set of person-centred key performance indicators and the corresponding measurement framework used to inform improvements in the experience of care. Design The study used an evaluation approach derived from the work of the Medical Research Council to assess the feasibility of the App and establish the degree to which the App was meeting the aims set out in the development phase. Evaluation data were collected using focus groups (n = 7) and semi-structured interviews (n = 7) to capture the impact of processes experienced by participating sites. Setting The study was conducted in the UK and Australia in two organizations, across 11 participating sites. Participants 22 nurses from 11 sites in two large health care organizations were recruited on a voluntary basis. Intervention Implementing the KPIs and measurement framework via the APP through two cycles of data collection. Main Outcome Measures The main outcome was to establish feasibility in the use of the App. Results The majority of nurse/midwife participants found the App easy to use. There was broad consensus that the App was an effective method to measure the patient experience and generated clear, concise reports in real time. Conclusions The implementation of the person-centred key performance indicators using the App enhanced the generation of meaningful data to evidence patient experience across a range of different clinical settings.

scholarly journals The impact of air–sea coupling and ocean biases on the seasonal cycle of southern West African precipitation

2019 ◽  
Vol 53 (11) ◽  
pp. 7027-7044
Author(s):  
Caroline M. Wainwright ◽  
Linda C. Hirons ◽  
Nicholas P. Klingaman ◽  
Richard P. Allan ◽  
Emily Black ◽  
...  
Keyword(s):  
West Africa ◽  
Atlantic Ocean ◽  
Crop Yields ◽  
Coupled Model ◽  
Dry Season ◽  
Global Ocean ◽  
Cmip5 Models ◽  
Rainfall Regime ◽  
Climate Projections ◽  
The Impact

Abstract The biannual seasonal rainfall regime over the southern part of West Africa is characterised by two wet seasons, separated by the ‘Little Dry Season’ in July–August. Lower rainfall totals during this intervening dry season may be detrimental for crop yields over a region with a dense population that depends on agricultural output. Coupled Model Intercomparison Project Phase 5 (CMIP5) models do not correctly capture this seasonal regime, and instead generate a single wet season, peaking at the observed timing of the Little Dry Season. Hence, the realism of future climate projections over this region is questionable. Here, the representation of the Little Dry Season in coupled model simulations is investigated, to elucidate factors leading to this misrepresentation. The Global Ocean Mixed Layer configuration of the Met Office Unified Model is particularly useful for exploring this misrepresentation, as it enables separating the effects of coupled model ocean biases in different ocean basins while maintaining air–sea coupling. Atlantic Ocean SST biases cause the incorrect seasonal regime over southern West Africa. Upper level descent in August reduces ascent along the coastline, which is associated with the observed reduction in rainfall during the Little Dry Season. When coupled model Atlantic Ocean biases are introduced, ascent over the coastline is deeper and rainfall totals are higher during July–August. Hence, this study indicates detrimental impacts introduced by Atlantic Ocean biases, and highlights an area of model development required for production of meaningful climate change projections over the West Africa region.

scholarly journals Response of Near-Surface Meteorological Conditions to Advection under Impact of the Green Roof

Atmosphere ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 759
Author(s):  
Haochen Tan ◽  
Pallav Ray ◽  
Mukul Tewari ◽  
James Brownlee ◽  
Ajaya Ravindran
Keyword(s):  
Surface Temperature ◽  
Urban Areas ◽  
Green Roof ◽  
Wrf Model ◽  
Single Layer ◽  
Meteorological Conditions ◽  
Rapid Urbanization ◽  
Near Surface ◽  
Budget Analysis ◽  
The Impact

Due to rapid urbanization, the near-surface meteorological conditions over urban areas are greatly modulated. To capture such modulations, sophisticated urban parameterizations with enhanced hydrological processes have been developed. In this study, we use the single-layer urban canopy model (SLUCM) available within the Weather Research and Forecasting (WRF) model to assess the response of near-surface temperature, wind, and moisture to advection under the impact of the green roof. An ensemble of simulations with different planetary boundary layer (PBL) schemes is conducted in the presence (green roof (GR)) and absence (control (CTL)) of green roof systems. Our results indicate that the near-surface temperature is found to be driven primarily by the surface heat flux with a minor influence from the zonal advection of temperature. The momentum budget analysis shows that both zonal and meridional momentum advection during the evening and early nighttime plays an important role in modulating winds over urban areas. The near-surface humidity remains nearly unchanged in GR compared to CTL, although the physical processes that determine the changes in humidity were different, in particular during the evening when the GR tends to have less moisture advection due to the reduced temperature gradient between the urban areas and the surroundings. Implications of our results are discussed.

scholarly journals MODELING THE IMPACT OF SURFACE CHARACTERISTICS ON THE NEAR SURFACE TEMPERATURE LAPSE RATE

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 395-399 ◽  
Author(s):  
M. K. Firozjaei ◽  
S. Fathololuomi ◽  
S. K. Alavipanah ◽  
M. Kiavarz ◽  
A. Vaezi ◽  
...  
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Feature Space ◽  
Surface Characteristics ◽  
Lapse Rate ◽  
Landsat 8 ◽  
Near Surface ◽  
Temperature Lapse Rate ◽  
Topographic Characteristics ◽  
The Impact

Abstract. Modeling of Near-Surface Temperature Lapse Rate (NSTLR) is very important in various environmental applications. The Land Surface Temperature (LST) is influenced by many properties and conditions including surface biophysical and topographic characteristics. Some researches have considered the LST - Digital Elevation Model (DEM) feature space to model NSTLR. However, the influence of detailed surface characteristics is rare. This study investigated the impact of surface characteristics on the LST-DEM feature space for NSTLR modeling. A set of remote sensing data including Landsat 8 images, MODIS products, and surface features including DEM and land use of the Balikhli-Chay on 01/07/2018, 18/08/2018 and 03/09/2018 were collected and used in this study. First, Split Window (SW) algorithm was used to estimate LST, and spectral indices were employed to model surface biophysical characteristics. Owing to the impact of surface biophysical and topographic characteristics on the LST-DEM feature space, the NSTLR was calculated for different classes of surface biophysical characteristics, land use, and solar local incident angle. The modeled NSTLR values based on the LST-DEM feature space on 01/07/2018, 18/08/2018 and 03/09/2018 were 8.5, 1.5 and 2.4 °C Km−1; respectively. The NSTLR in different classes of surface biophysical characteristics, land use type and topographical parameters were variable between 0.5 to 14 °C Km−1. This clearly showed the dependence of NSTLR on topographic and biophysical conditions. This provides a new way of calculating surface characteristic specific NSTLR.

A Package of New Universal Non-Iterative Parametrizations for Stable Surface Layer Transfer Coefficients of Momentum, Heat and Moisture in Numerical Earth System Models

2021 ◽  
Author(s):  
Vladimir Gryanik ◽  
Christof Luepkes ◽  
Andrey Grachev ◽  
Dmitry Sidorenko
Keyword(s):  
Climate Models ◽  
Similarity Theory ◽  
Weather Prediction ◽  
Climate Projections ◽  
Transfer Coefficients ◽  
Near Surface ◽  
Stability Functions ◽  
Stable Surface Layer ◽  
Heat And Moisture ◽  
The Impact

<p><span>Results of weather forecast, present-day climate simulations and future climate projections depend among other factors on the interaction between the atmosphere and the underlying sea-ice, the land and the ocean. In numerical weather prediction and climate models some of these interactions are accounted for by transport coefficients describing turbulent exchange of momentum, heat and moisture. Currently used transfer coefficients have, however, large uncertainties in flow regimes being typical for cold nights and seasons, but especially in the polar regions. Furthermore, their determination is numerically complex. It is obvious that progress could be achieved when the transfer coefficients would be given by simple mathematical formulae in frames of an economic computational scheme. Such a new universal, so-called non-iterative parametrization scheme is derived for a package of transfer coefficients.</span></p><p><span>The derivation is based on the Monin-Obukhov similarity theory, which is over the years well accepted in the scientific community. The newly derived non-iterative scheme provides a basis for a cheap systematic study of the impact of near-surface turbulence and of the related transports of momentum, heat and moisture in NWP and climate models. </span></p><p><span>We show that often used transfer coefficients like those of Louis et al. (1982) or of Cheng and Brutsaert (2005) can be applied at large stability only with some caution, keeping in mind that at large stability they significantly overestimate the transfer coefficient compared with most comprehensive measurements. The latter are best reproduced by Gryanik et al. (2020) functions, which are part of the package. We show that the new scheme is flexible, thus, new stability functions can be added to the package, if required. </span></p><p> </p><p> <span>Gryanik, V.M., Lüpkes, C., Grachev, A., Sidorenko, D. (2020) New Modified and Extended Stability Functions for the Stable Boundary Layer based on SHEBA and Parametrizations of Bulk Transfer Coefficients for Climate Models, J. Atmos. Sci., 77, 2687-2716</span></p><p><br><br></p>

scholarly journals Representation of model error in a convective-scale ensemble prediction system

2014 ◽  
Vol 21 (1) ◽  
pp. 19-39 ◽  
Author(s):  
L. H. Baker ◽  
A. C. Rudd ◽  
S. Migliorini ◽  
R. N. Bannister
Keyword(s):  
Model Error ◽  
Ensemble Prediction ◽  
Initial Condition ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Near Surface ◽  
Skill Scores ◽  
The Uk ◽  
Parameter Values ◽  
The Impact

Abstract. In this paper ensembles of forecasts (of up to six hours) are studied from a convection-permitting model with a representation of model error due to unresolved processes. The ensemble prediction system (EPS) used is an experimental convection-permitting version of the UK Met Office's 24-member Global and Regional Ensemble Prediction System (MOGREPS). The method of representing model error variability, which perturbs parameters within the model's parameterisation schemes, has been modified and we investigate the impact of applying this scheme in different ways. These are: a control ensemble where all ensemble members have the same parameter values; an ensemble where the parameters are different between members, but fixed in time; and ensembles where the parameters are updated randomly every 30 or 60 min. The choice of parameters and their ranges of variability have been determined from expert opinion and parameter sensitivity tests. A case of frontal rain over the southern UK has been chosen, which has a multi-banded rainfall structure. The consequences of including model error variability in the case studied are mixed and are summarised as follows. The multiple banding, evident in the radar, is not captured for any single member. However, the single band is positioned in some members where a secondary band is present in the radar. This is found for all ensembles studied. Adding model error variability with fixed parameters in time does increase the ensemble spread for near-surface variables like wind and temperature, but can actually decrease the spread of the rainfall. Perturbing the parameters periodically throughout the forecast does not further increase the spread and exhibits "jumpiness" in the spread at times when the parameters are perturbed. Adding model error variability gives an improvement in forecast skill after the first 2–3 h of the forecast for near-surface temperature and relative humidity. For precipitation skill scores, adding model error variability has the effect of improving the skill in the first 1–2 h of the forecast, but then of reducing the skill after that. Complementary experiments were performed where the only difference between members was the set of parameter values (i.e. no initial condition variability). The resulting spread was found to be significantly less than the spread from initial condition variability alone.

scholarly journals Advancing land surface model development with satellite-based Earth observations

2017 ◽  
Vol 21 (5) ◽  
pp. 2483-2495 ◽  
Author(s):  
Rene Orth ◽  
Emanuel Dutra ◽  
Isabel F. Trigo ◽  
Gianpaolo Balsamo
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Weather Forecasting ◽  
Model Performance ◽  
Land Surface Model ◽  
Climate System ◽  
Surface Model ◽  
Earth Observations ◽  
The Impact

Abstract. The land surface forms an essential part of the climate system. It interacts with the atmosphere through the exchange of water and energy and hence influences weather and climate, as well as their predictability. Correspondingly, the land surface model (LSM) is an essential part of any weather forecasting system. LSMs rely on partly poorly constrained parameters, due to sparse land surface observations. With the use of newly available land surface temperature observations, we show in this study that novel satellite-derived datasets help improve LSM configuration, and hence can contribute to improved weather predictability. We use the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) and validate it comprehensively against an array of Earth observation reference datasets, including the new land surface temperature product. This reveals satisfactory model performance in terms of hydrology but poor performance in terms of land surface temperature. This is due to inconsistencies of process representations in the model as identified from an analysis of perturbed parameter simulations. We show that HTESSEL can be more robustly calibrated with multiple instead of single reference datasets as this mitigates the impact of the structural inconsistencies. Finally, performing coupled global weather forecasts, we find that a more robust calibration of HTESSEL also contributes to improved weather forecast skills. In summary, new satellite-based Earth observations are shown to enhance the multi-dataset calibration of LSMs, thereby improving the representation of insufficiently captured processes, advancing weather predictability, and understanding of climate system feedbacks.

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