DO4models- Dust Observations for models: Linking a new dust source-area data set to improved physically-based dust emission schemes in climate models
Lead Research Organisation:
University of Oxford
Department Name: Geography - SoGE
Abstract
Dust is an important part of the Earth's land-atmosphere-ocean-biosphere system affecting climate, the fertility of oceans, plant communities on land, and human health. Wind is able to move vast amounts of dust over the Earth's surface and into the atmosphere. North Africa alone emits 500-1000 million tons of dust a year. To predict future weather and climate it is crucial that numerical models, our key tool for such prediction, represent the relationships important to the emission, transport and deposition of dust. Excluding dust from models leads to large local and global errors. Accurate modelling of dust begins with the correct simulation of emission. This is vital because source area simulation errors lead to errors in local climate dynamics and incorrect dust transport. However, many of the major dust source regions of the world are in extremely remote places for which there is no ground-based data on dust emission or its controls. Although recent advances have been made in identifying major dust sources, for example from satellite data, many models of dust emission are still very simple and are not constrained by real observed data. In the drive to predict weather and climate at spatial scales useful for planning decisions, numerical models have increased their resolution so that some global models run at near 1 degree and many regional models at better than 0.2 degree resolution. The few observed data sets characterising dust source areas and behaviour that do exist simply do not support the scale at which these models are being run. It is therefore extremely difficult to either evaluate or improve the dust emission component of models as things stand. Simulation of dust source areas is consequently very inaccurate and is set to remain so. We propose to address this problem by developing the first model dust emission scheme which is based on purpose built observed data sets that have been deliberately constructed to exactly match the scale of regional climate models. We propose to do this by first using high-resolution satellite data to identify key sources of dust within field areas that are characteristic of dust source areas found in many parts of the world. We will then use state-of-the-art field equipment to systematically investigate the real processes that control dust emission at the model grid box scale, measuring background conditions over a long period as well as the important processes that occur during dust storms. We will therefore measure and monitor both the factors that control the availability of dust to the wind on the ground (erodibility), and the ability of the wind to move that sediment (erosivity) to create this definitive data set on source regions that can be used in model development for years to come. We will be able to determine for the first time what kind of dust source data (e.g. surface roughness, soil moisture, wind gustiness) lead to the largest improvement in the observationally-constrained model emission scheme. We will also be able to say what errors result in simulations if no field data is collected and only remotely sensed data are used as inputs to the models. This will provide important guidance on how and where to spend time and money in the improvement of climate models in the future and also to provide direction on what kind of field data are most important to collect. The Met Office does not have the capacity to undertake the extensive fieldwork required to deliver the observational data that are critical to model development. Our proposal cuts across the traditional barriers between field work, Earth Observation and numerical modelling. It is only by doing so that breakthroughs in dust numerical modelling will be achieved. Our unprecedented field observations which are tailored to numerical model needs will be a significant step towards a new generation of model schemes.
Publications
Nield J
(2016)
Evaporative sodium salt crust development and its wind tunnel derived transport dynamics under variable climatic conditions
in Aeolian Research
Nield J
(2014)
Detecting surface moisture in aeolian environments using terrestrial laser scanning
in Aeolian Research
Von Holdt J
(2021)
Influence of sampling approaches on physical and geochemical analysis of aeolian dust in source regions
in Aeolian Research
Dansie A
(2017)
Measurements of windblown dust characteristics and ocean fertilization potential: The ephemeral river valleys of Namibia
in Aeolian Research
Gadal C
(2022)
Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements
in Boundary-Layer Meteorology
Thomas D
(2017)
Holocene fluvial valley fill sources of atmospheric mineral dust in the Skeleton Coast, Namibia
in Earth Surface Processes and Landforms
Dansie A
(2017)
Spatial variability of ocean fertilizing nutrients in the dust-emitting ephemeral river catchments of Namibia
in Earth Surface Processes and Landforms
Nield J
(2015)
Climate-surface-pore-water interactions on a salt crusted playa: implications for crust pattern and surface roughness development measured using terrestrial laser scanning
in Earth Surface Processes and Landforms
Richards J
(2021)
Uneven surface moisture as a driver of dune formation on ephemeral lake beds under conditions similar to the present day: A model-based assessment from the Makgadikgadi Basin, northern Botswana
in Earth Surface Processes and Landforms
Nield J
(2015)
The dynamism of salt crust patterns on playas
in Geology
Description | Constrained the variability of dust emissions on a 'homogenous' major southern hemisphere source at a scale sympathetic with that of a climate model. |
Exploitation Route | Climate model dust emission module construction. |
Sectors | Aerospace Defence and Marine Education Environment Transport |
URL | http://africanclimateoxford.net/projects/do4-models/ |
Title | Data used in 'Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements.' |
Description | This repository contains the data used in: Gadal, C., Delorme, P., Narteau, C. et al. Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements. Boundary-Layer Meteorol 185, 309-332 (2022). https://doi.org/10.1007/s10546-022-00733-6 where wind data measured at 4 different places in and across the Namib Sand Sea are compared to the data from the ERA5/ERA5Land climate reanalyses. The use this data, one should first look at the GitHub repository https://github.com/Cgadal/GiantDunes and at the corresponding documentation https://cgadal.github.io/GiantDunes/. The description sometimes refers to scripts used in https://github.com/Cgadal/GiantDunes/tree/master/Processing. The two folders 'raw_data' and 'processed_data' contain the input raw_data, and the output data after processing used to make the paper figures, respectively. In each of them, '.npy' files contain Python dictionaries with different variables in them. They can be loaded using the Python library numpy as data = np.load('file.npy', allow_pickle=True).item(); and the different keys (variables) can be printed with data.keys() or data[station].keys() if data.keys() return the different stations. Unless specified otherwise below, note that all variables are given in the International System of Units (SI), and wind direction is given anticlockwise, with the 0 being a wind blowing from the West to the East. raw_data: DEM: contains the Digital Elevation Models of the two stations from the SRTM30, downloaded from here: https://dwtkns.com/srtm30m/ ERA5: hourly data from the ER5 climate reanalysis, on surface (_BLH) and pressure levels (_levels). Downloaded from https://cds.climate.copernicus.eu/ ERA5Land: hourly data from the ER5Land climate reanalysis Downloaded from https://cds.climate.copernicus.eu/ KML_points: kml points of the measurement station. It can be opened directly in GoogleEarth. measured_wind_data: contains the measured in situ data. The windspeed is measured using Vector Instruments A100-LK cup anemometers, the wind direction using Vector Instruments W200-P wind vane and the time using Campbell Instruments CR10X and CR1000X dataloggers. processed_data: 'Data_preprocessed.npy': preprocessed_data, output of 1_data_preprocessing_plot.py 'Data_DEM.npy': properties of the processed DEM, the output of 2_DEM_analysis_plot.py 'Data_calib_roughness.npy': data from the calibration of the hydrodynamic roughnesses, the output of 3_roughness_calibration_plot.py 'Data_final.npy': file containing all computed quantities 'time_series_hydro_coeffs.npy': file containing the time series of the calculated hydrodynamic coefficients by '5_norun_hydro_coeff_time_series.npy'. Depending on the loaded data file, main dictionary keys can be: 'lat': latitude, in degree 'lon': longitude, in degree 'time': time vector, in datetime objects (https://docs.python.org/3/library/datetime.html) 'DEM': elevation data array in [m], with dimensions matching 'lat' and 'lon' vectors 'z_mes', 'z_insitu', 'z_ERA5LAND': height of the corresponding velocity 'direction': measured wind direction, in [degrees] 'velocity': measured wind velocity, in [m/s] 'orientaion': dune pattern orientation, [deg] 'wavelength': dune pattern wavelength, [km] 'z0_insitu': chosen hydrodynamic roughness for the considered station. 'U_insitu', 'Orientation_insitu': hourly averaged measured wind velocities and direction 'U_era', 'Orientation_era': hourly 10m wind data from the ERA5Land data set 'Boundary layer height', 'blh': boundary layer height from the hourly ERA5 dataset 'Pressure levels', 'levels': Pressure levels from the pressure levels ERA5 dataset 'Temperature', 't': Temperature from the pressure levels ERA5 dataset 'Specific humidity', 'q': Specific humidity from the pressure levels ERA5 dataset 'Geopotential', 'z': Geopotential from the pressure levels ERA5 dataset 'Virtual_potential_temperature': Virtual potential temperature calculated from the pressure levels ERA5 dataset 'Potential_temperature': Potential temperature calculated from the pressure levels ERA5 dataset 'Density': Density calculated from the pressure levels ERA5 dataset 'height': Vertical coordinates calculated from the pressure levels ERA5 dataset 'theta_ground': Averaged virtual potential temperature within the ABL. 'delta_theta': Virtual potential temperature at the ABL. 'gradient_free_atm': Virtual potential temperature gradient in the FA. 'Froude': time series of the Froude number U/((delta_theta/theta_ground)*g*BLH) 'kH': time series of the number 'kH' 'kLB': time series of the internal Froude number kU/N Other keys are not relevant and are stored for verification purposes. For more details, please contact Cyril Gadal (see authors), and look at the following GitHub repository: https://github.com/Cgadal/GiantDunes, where all the codes are present. |
Type Of Material | Database/Collection of data |
Year Produced | 2022 |
Provided To Others? | Yes |
URL | https://zenodo.org/doi/10.5281/zenodo.6343137 |