22 Years of Near-zero Cloud Cover Variability across Varying Geographical Landscapes of Southern Africa: A Surprising Anomaly

In physical geography, clouds are known to dictate global energy budgets and to have crucial ripple effects on other climatic variables such as diurnal range of temperature (DTR), a key indicator of climate change. Here, a 115-year state-of-the-art station based gridded dataset from the Climatic Research Unit (CRU) is interrogated to understand the evolution of cloud cover across southern Africa for the period 1901 - 2016. Results show near-constant variability from 1901 – 1922. It was therefore hypothesised that the observed near-constant variability would result in a similar pattern for some climatic variables such as DTR as the opposite would bring into question our current knowledge of geographical mechanisms underlying DTR control across the region. Further analyses showed little to no association between cloud cover and other climatological variables (including DTR) for the period 1901 – 1922 but strong and significant association from 1923 – 2016. This is the first observational evidence of near-constant cloud cover variability; it is surprising, and counterintuitive. This constant variation can be attributed to limited ground-based observations that went into the construction of the CRU gridded dataset during the 1901 – 1922 period and therefore, caution needs to be exercised by studies that need to use the data for the said period. This is a crucial area of scientific enquiry, and a lack of caution can lead to misleading conclusions on cloud cover evolution and how that relates to climate change.

A gridded European global dataset based on in-situ observations

<p>Within the EOBS project, one of the objectives is to provide an (ensemble) gridded data set of global radiation. <em>In-situ</em> observations of daily sums of global radiation are combined with daily sunshine duration records to construct a dataset for daily global radiation that goes back to 1950. A generalization of the commonly used Angstrom-Prescott formula is used to relate daily values of sunshine duration to global radiation, where optimal values of the parameters in this model are found by allowing for variations in the latitude and with the seasons. A quality control procedure based on the physical limits of  global radiation - latitude and yearday dependent - is applied to the data.</p><p>Based on this dataset, a gridded dataset for daily global radiation is produced with a resolution of 0.1 degree, covering Europe. The density of the combined networks of radiation and sunshine duration measurements hugely varies in space and time and this inhomogeneity is likely to give variations in space and time of the confidence of the gridded dataset. A method for enhancing the spatial analysis of daily global radiation from a sparse network is by incorporating information on the spatial covariance in the global radiation fields determined from high‐resolution measurements available in the past. Here we use satellite-based daily observations of downwards surface shortwave radiation from the CERES (Clouds and the Earth's Radiant Energy System) dataset for this purpose.</p><p>This approach is inspired by the reduced space optimal interpolation (RSOI) method, and the dominant patterns of variability are calculated using Self Organizing Maps (SOMs). Before reducing the dimension of the CERES dataset to 15 patterns, seasonal trends were removed. SOMs comprise a class of unsupervised neural networks that organize input geospatial data into a user-defined number of outputs (nodes) obtained by iteratively adjusting the nodes to resemble the input data. The training of this unsupervised artificial neural network is entirely data driven.</p><p>In the presentation, the similarity between the gridded dataset and the underlying station data is quantified, and a comparison against the CMSAF SARAH dataset is presented.</p>

A high-resolution gridded dataset of daily temperature and precipitation records (1980–2018) for Trentino-South Tyrol (north-eastern Italian Alps)

A high-resolution gridded dataset of daily mean temperature and precipitation series spanning the period 1980–2018 was built for Trentino-South Tyrol, a mountainous region in north-eastern Italy, starting from an archive of observation series from more than 200 meteorological stations and covering the regional domain and surrounding countries. The original station data underwent a processing chain including quality and consistency checks, homogeneity tests, with the homogenization of the most relevant breaks in the series, and a filling procedure of daily gaps aiming at maximizing the data availability. Using the processed database, an anomaly-based interpolation scheme was applied to project the daily station observations of mean temperature and precipitation onto a regular grid of 250 m × 250 m resolution. The accuracy of the resulting dataset was evaluated by leave-one-out station cross-validation. Averaged over all sites, interpolated daily temperature and precipitation show no bias, with a mean absolute error (MAE) of about 1.5 ∘C and 1.1 mm and a mean correlation of 0.97 and 0.91, respectively. The obtained daily fields were used to discuss the spatial representation of selected past events and the distribution of the main climatological features over the region, which shows the role of the mountainous terrain in defining the temperature and precipitation gradients. In addition, the suitability of the dataset to be combined with other high-resolution products was evaluated through a comparison of the gridded observations with snow-cover maps from remote sensing observations. The presented dataset provides an accurate insight into the spatio-temporal distribution of temperature and precipitation over the mountainous terrain of Trentino-South Tyrol and a valuable support for local and regional applications of climate variability and change. The dataset is publicly available at https://doi.org/10.1594/PANGAEA.924502 (Crespi et al., 2020).

A new merged dataset for analyzing clouds, precipitation and atmospheric parameters based on ERA5 reanalysis data and the measurements of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar and visible and infrared scanner

Clouds and precipitation have vital roles in the global hydrological cycle and the radiation budget of the atmosphere–Earth system and are closely related to both the regional and the global climate. Changes in the status of the atmosphere inside clouds and precipitation systems are also important, but the use of multi-source datasets is hampered by their different spatial and temporal resolutions. We merged the precipitation parameters measured by the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) with the multi-channel cloud-top radiance measured by the visible and infrared scanner (VIRS) and atmospheric parameters in the ERA5 reanalysis dataset. The merging of pixels between the precipitation parameters and multi-channel cloud-top radiance was shown to be reasonable. The 1B01-2A25 dataset of pixel-merged data (1B01-2A25-PMD) contains cloud parameters for each PR pixel. The 1B01-2A25 gridded dataset (1B01-2A25-GD) was merged spatially with the ERA5 reanalysis data. The statistical results indicate that gridding has no unacceptable influence on the parameters in 1B01-2A25-PMD. In one orbit, the difference in the mean value of the near-surface rain rate and the signals measured by the VIRS was no more than 0.87 and the standard deviation was no more than 2.38. The 1B01-2A25-GD and ERA5 datasets were spatiotemporally collocated to establish the merged 1B01-2A25 gridded dataset (M-1B01-2A25-GD). Three case studies of typical cloud and precipitation events were analyzed to illustrate the practical use of M-1B01-2A25-GD. This new merged gridded dataset can be used to study clouds and precipitation systems and provides a perfect opportunity for multi-source data analysis and model simulations. The data which were used in this paper are freely available at https://doi.org/10.5281/zenodo.4458868 (Sun and Fu, 2021).

Measurement report: regional trends of stratospheric ozone evaluated using the MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP)

In this paper, we present the MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP) in the stratosphere with a resolved longitudinal structure, which is derived from data from six limb and occultation satellite instruments: GOMOS, SCIAMACHY and MIPAS on Envisat, OSIRIS on Odin, OMPS on Suomi-NPP, and MLS on Aura. The merged dataset was generated as a contribution to the European Space Agency Climate Change Initiative Ozone project (Ozone_cci). The period of this merged time series of ozone profiles is from late 2001 until the end of 2018. The monthly mean gridded ozone profile dataset is provided in the altitude range from 10 to 50 km in bins of 10∘ latitude × 20∘ longitude. The merging is performed using deseasonalized anomalies. The created MEGRIDOP dataset can be used for analyses that probe our understanding of stratospheric chemistry and dynamics. To illustrate some possible applications, we created a climatology of ozone profiles with resolved longitudinal structure. We found zonal asymmetry in the climatological ozone profiles at middle and high latitudes associated with the polar vortex. At northern high latitudes, the amplitude of the seasonal cycle also has a longitudinal dependence. The MEGRIDOP dataset has also been used to evaluate regional vertically resolved ozone trends in the stratosphere, including the polar regions. It is found that stratospheric ozone trends exhibit longitudinal structures at Northern Hemisphere middle and high latitudes, with enhanced trends over Scandinavia and the Atlantic region. This agrees well with previous analyses and might be due to changes in dynamical processes related to the Brewer–Dobson circulation.

A new global gridded glacier dataset based on the Randolph Glacier Inventory version 6.0

Gridded glacier datasets are essential for various glaciological and climatological research because they link glacier cover with the corresponding gridded meteorological variables. However, there are significant differences between the gridded data and the shapefile data in the total area calculations in the Randolph Glacier Inventory (RGI) 6.0 at global and regional scales. Here, we present a new global gridded glacier dataset based on the RGI 6.0 that eliminates the differences. The dataset is made by dividing the glacier polygons using cell boundaries and then recalculating the area of each polygon in the cell. Our dataset (1) exhibits a good agreement with the RGI area values for those regions in which gridded areas showed a generally good consistency with those in the shapefile data, and (2) reduces the errors existing in the current RGI gridded dataset. All data and code used in this study are freely available and we provide two examples to demonstrate the application of this new gridded dataset.

Carbon Monitor Gridded Dataset (CMGD), a near-real-time high resolution gridded CO2 emission estimates dataset

<p>The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO<sub>2</sub>) emissions. This research first presented near-real-time high-spatial-resolution(0.1°*0.1°) and high-temporal-resolution(daily) gridded estimates of CO<sub>2</sub> emissions for different sectors named Carbon Monitor Gridded Dataset(CMGD). This dataset responds to the growing and urgent need for high-quality, fine-grained CO<sub>2</sub> emission estimates to support global emissions monitoring on the refined spatial scale. CMGD is derived from our Carbon Monitor, a near-real-time daily dataset of global CO<sub>2</sub> emission from fossil fuel and cement production, including detailed information in 6 sectors and main countries. Based on EDGAR v5.0 gridded CO<sub>2</sub> emissions map and other geospatial proxies, we finally constructed CMGD with a high spatial resolution of 0.1 degree. Here, we provided the total emissions of specific countries and analyzed the countries with larger emissions (including the EU). Furthermore, we analyzed the daily emission changes of several typical cities around the world and provided insights on the contributions of various sectors. Through CMGD, we can get a much faster and more fine-grained overview, including timelines that show where and how emissions decreases have corresponded to lockdown measures at the finer spatial scales. The fine-grain and timeliness of CMGD emissions estimates will facilitate more local and adaptive management of CO<sub>2</sub> emissions during both the pandemic recovery and the ongoing energy transition.</p>

A new merged dataset for analyzing clouds, precipitation and atmospheric parameters based on ERA5 reanalysis data and the measurements of TRMM PR and VIRS

Clouds and precipitation have vital roles in the global hydrological cycle and the radiation budget of the atmosphere–Earth system and are closely related to both the regional and global climate. Changes in the status of the atmosphere inside clouds and precipitation systems are also important, but the use of multi-source datasets is hampered by their different spatial and temporal resolutions. We merged the precipitation parameters measured by the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) with the multi-channel cloud-top radiance measured by the Visible and Infrared Scanner (VIRS) and atmospheric parameters in the ERA5 reanalysis dataset. The merging of pixels between the precipitation parameters and multi-channel cloud-top radiance was shown to be reasonable. The 1B01-2A25 dataset of pixel-merged data (1B01-2A25-PMD) contains cloud parameters for each PR pixel. The 1B01-2A25 gridded dataset (1B01-2A25-GD) was merged spatially with the ERA5 reanalysis data. The statistical results indicate that gridding has no unacceptable influence on the parameters in the 1B01-2A25-PMD. In one orbit, the difference in the mean value of the near-surface rain rate and the signals measured by the VIRS was no more than 0.87 and the standard deviation was no more than 2.38. The 1B01-2A25-GD and ERA5 datasets were spatiotemporally collocated to establish the merged 1B01-2A25 gridded dataset (M-1B01-2A25-GD). Three case studies of typical cloud and precipitation events were analyzed to illustrate the practical use of the M-1B01-2A25-GD. This new merged gridded dataset can be used to study clouds and precipitation systems and provides a perfect opportunity for multi-source data analysis and model simulations. The data which were used in this paper are freely available at http://doi.org/10.5281/zenodo.4458868 (Sun and Fu,2021).