scholarly journals Detection, Attribution, and Future Response of Global Soil Moisture in Summer

2021 ◽  
Vol 9 ◽  
Author(s):  
Liang Qiao ◽  
Zhiyan Zuo ◽  
Dong Xiao ◽  
Lulei Bu
Keyword(s):  
Soil Moisture ◽  
Coupled Model ◽  
Global Scale ◽  
Anthropogenic Aerosols ◽  
External Forcings ◽  
Global Soil ◽  
Carbon Dioxide Fertilization ◽  
Deep Layers ◽  
High Emission ◽  
Drying Trend

Soil moisture variations and its relevant feedbacks (e.g., soil moisture–temperature and soil moisture–precipitation) have a crucial impact on the climate system. This study uses reanalysis and Coupled Model Intercomparison Project phase 6 simulations datasets to detect, attribute, and project soil moisture variations. The effect of anthropogenic forcings [greenhouse gases (GHG), anthropogenic aerosols (AA), and land use (LU) change] on soil moisture is much larger than that of the natural forcing. Soil moisture shows a drying trend at a global scale, which is mainly attributed to GHG forcing. The effects of external forcings vary with the regions significantly. Over eastern South America, GHG, AA, and natural forcings make soil dry, while LU forcing makes the soil wet. Over severely drying Europe, all the external forcings including GHG, AA, LU, and natural forcing exhibit drying effect. The optimal fingerprint method detection results show that some of GHG, AA, LU, and natural signals can be detected in soil moisture variations in some regions such as Europe. The soil will keep drying in all scenarios over most parts of the globe except Sahel and parts of mid-latitudes of Asia. With the increase of anthropogenic emissions, the variation of global soil moisture will be more extreme, especially in hotspots where the land–atmosphere coupling is intensive. The drying trend of soil moisture will be much larger on the surface than in middle and deep layers in the future, and this phenomenon will be more severe under the high-emission scenario. It may be affected by increased evaporation and the effect of carbon dioxide fertilization caused by global warming.

Forced modulations of Sahel rainfall at decadal timescale over the 20th Century.

2021 ◽  
Author(s):  
Cassien Diabe Ndiaye ◽  
Juliette Mignot ◽  
Elsa Mohino
Keyword(s):  
North Atlantic Ocean ◽  
Coupled Model ◽  
Internal Variability ◽  
Boreal Summer ◽  
Semiarid Region ◽  
Rainfall Regime ◽  
Anthropogenic Aerosols ◽  
Coupled Models ◽  
External Forcings ◽  
Sahel Precipitation

<p>The semiarid region of the Sahel was marked during the 20<sup>th</sup> Century by significant modulations of its rainfall regime. Part of these modulations has been associated with the internal variability of the climate system, mediated by changes in oceanic sea surface temperature (SST). We show here that the external forcings, and in particular anthropogenic aerosols, might have played a role more important than previously thought in setting these variations. The study is based on the recent simulations performed for CMIP6 with the IPSL-CM6A-LR coupled model. As in most coupled models, the maximum precipitation is limited to the southern Sahel during boreal summer in the IPSL-CM6A-LR model. A novel definition of the Sahel precipitation region is proposed in order to take this bias into account. Our results show that external forcings induce decadal modulations of Sahel precipitation that correlate significantly at 0.6 with the observed precipitations and that the anthropogenic aerosols explain more than 70% of these modulations. These results confirm recent results of CMIP6 highlighting an important role of aerosol forcing for the decadal climate in and around the North Atlantic ocean.</p>

Evaluation of soil moisture in CMIP6 simulations

2021 ◽  
pp. 1-55
Author(s):  
Liang Qiao ◽  
Zhiyan Zuo ◽  
Dong Xiao
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Coupled Model ◽  
Cold Season ◽  
Global Scale ◽  
Reanalysis Data ◽  
Limiting Factor ◽  
The Tibetan Plateau ◽  
Frozen Ground ◽  
Deep Soil

AbstractThis study employs multiple reanalysis datasets to evaluate the global shallow and deep soil moisture in Coupled Model Intercomparison Project phase 6 (CMIP6) simulations. The multi-model ensemble mean produces generally reasonable simulations for overall climatology, wet and dry centers, and annual peaks in the melt season at mid-high latitudes and the rainy season at low latitudes. The simulation capability for shallow soil moisture depends on the relationship between soil moisture and the difference between precipitation and evaporation (P–E). Although most models produce effective simulations in regions where soil moisture is significantly related to the P–E (e.g., Europe, low-latitude Asia, and the Southern Hemisphere), considerable discrepancies between simulated conditions and reanalysis data occur at high elevations and latitudes (e.g., Siberia and the Tibetan Plateau), where cold season processes play a driving role in soil moisture variability. These discrepancies reflect the lack of information concerning the thaw of snow and frozen ground in the reanalyzed data and the inability of models to simulate these processes. The models also perform poorly in areas of extreme aridity. On a global scale, the majority of models provide consistent and capable simulations owing to the minimal variability in deep soil moisture and limited observational information in reanalysis data. Models with higher spatial resolution do not exhibit closer agreement with the reanalysis data, indicating that spatial resolution is not the first limiting factor for CMIP6 soil moisture simulations.

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

scholarly journals NOAA Satellite Soil Moisture Operational Product System (SMOPS) Version 3.0 Generates Higher Accuracy Blended Satellite Soil Moisture

2020 ◽  
Vol 12 (17) ◽  
pp. 2861
Author(s):  
Jifu Yin ◽  
Xiwu Zhan ◽  
Jicheng Liu
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Land Surface ◽  
Vital Role ◽  
Product System ◽  
Global Soil ◽  
One Stop ◽  
Satellite Sensors ◽  
Blended Data

Soil moisture plays a vital role for the understanding of hydrological, meteorological, and climatological land surface processes. To meet the need of real time global soil moisture datasets, a Soil Moisture Operational Product System (SMOPS) has been developed at National Oceanic and Atmospheric Administration to produce a one-stop shop for soil moisture observations from all available satellite sensors. What makes the SMOPS unique is its near real time global blended soil moisture product. Since the first version SMOPS publicly released in 2010, the SMOPS has been updated twice based on the users’ feedbacks through improving retrieval algorithms and including observations from new satellite sensors. The version 3.0 SMOPS has been operationally released since 2017. Significant differences in climatological averages lead to remarkable distinctions in data quality between the newest and the older versions of SMOPS blended soil moisture products. This study reveals that the SMOPS version 3.0 has overwhelming advantages of reduced data uncertainties and increased correlations with respect to the quality controlled in situ measurements. The new version SMOPS also presents more robust agreements with the European Space Agency’s Climate Change Initiative (ESA_CCI) soil moisture datasets. With the higher accuracy, the blended data product from the new version SMOPS is expected to benefit the hydrological, meteorological, and climatological researches, as well as numerical weather, climate, and water prediction operations.

scholarly journals Cloud albedo changes in response to anthropogenic sulfate and non-sulfate aerosol forcings in CMIP5 models

2017 ◽  
Vol 17 (14) ◽  
pp. 9145-9162 ◽  
Author(s):  
Lena Frey ◽  
Frida A.-M. Bender ◽  
Gunilla Svensson
Keyword(s):  
Water Content ◽  
Liquid Water ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Liquid Water Path ◽  
Anthropogenic Aerosols ◽  
Aerosol Composition ◽  
Cloud Albedo ◽  
Cloud Water Content ◽  
Absorbing Aerosols

Abstract. The effects of different aerosol types on cloud albedo are analysed using the linear relation between total albedo and cloud fraction found on a monthly mean scale in regions of subtropical marine stratocumulus clouds and the influence of simulated aerosol variations on this relation. Model experiments from the Coupled Model Intercomparison Project phase 5 (CMIP5) are used to separately study the responses to increases in sulfate, non-sulfate and all anthropogenic aerosols. A cloud brightening on the month-to-month scale due to variability in the background aerosol is found to dominate even in the cases where anthropogenic aerosols are added. The aerosol composition is of importance for this cloud brightening, that is thereby region dependent. There is indication that absorbing aerosols to some extent counteract the cloud brightening but scene darkening with increasing aerosol burden is generally not supported, even in regions where absorbing aerosols dominate. Month-to-month cloud albedo variability also confirms the importance of liquid water content for cloud albedo. Regional, monthly mean cloud albedo is found to increase with the addition of anthropogenic aerosols and more so with sulfate than non-sulfate. Changes in cloud albedo between experiments are related to changes in cloud water content as well as droplet size distribution changes, so that models with large increases in liquid water path and/or cloud droplet number show large cloud albedo increases with increasing aerosol. However, no clear relation between model sensitivities to aerosol variations on the month-to-month scale and changes in cloud albedo due to changed aerosol burden is found.

scholarly journals Development of a hydrometeorological forcing data set for global soil moisture estimation

2005 ◽  
Vol 25 (13) ◽  
pp. 1697-1714 ◽  
Author(s):  
A. A Berg ◽  
J. S. Famiglietti ◽  
M. Rodell ◽  
R. H. Reichle ◽  
U. Jambor ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Set ◽  
Global Soil ◽  
Moisture Estimation

Potential of Mapping Global Soil from SMAP Soil Moisture Product: A Pilot Study

2021 ◽  
pp. 1-1
Author(s):  
Long Zhao ◽  
Kun Yang ◽  
Jie He ◽  
Hui Zheng ◽  
Donghai Zheng
Keyword(s):  
Soil Moisture ◽  
Pilot Study ◽  
Global Soil

SOURCES OF GLOBAL SCALE SOIL MOISTURE MONITORING DATA BY SATELLITE BASED REMOTE SENSING OF EARTH’S SURFACE

2021 ◽  
Vol 100 (1) ◽  
pp. 36-41
Author(s):  
A.A. Volchek ◽  
◽  
D.O. Petrov ◽  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Global Scale ◽  
Research Papers ◽  
Equivalent Thickness ◽  
Satellite Gravimetry ◽  
Volumetric Soil Moisture ◽  
Characteristic Features ◽  
Agricultural Regions ◽  
Land Water

A review of modern tools of global monitoring of soil moisture by means of remote sensing of the Earth’s surface is presented. The characteristic features of the use of orbital radiometers and radars of C, X and L microwave bands for estimating the volumetric soil moisture at a depth of 5 cm and the root layer of vegetation are considered. A review of the capabilities of satellite gravimetry to assess the land water equivalent thickness is made. A number of sources have been proposed for obtaining estimates of soil water content from satellite based radiometric devices and orbital gravimetric systems. Based on the analysis of scientific research papers, the complexity of monitoring the level of fire danger indices in forests is shown, and the prospects of assessing soil moisture in agricultural regions using microwave orbital instruments are demonstrated, and the adequacy of calculating the moisture content in soil at a depth of up to one meter using satellite gravimetry is described.

scholarly journals Discrepancies of surface temperature trends in the CMIP5 simulations and observations on the global and regional scales

2013 ◽  
Vol 9 (6) ◽  
pp. 6161-6178 ◽  
Author(s):  
L. Zhao ◽  
J. Xu ◽  
A. M. Powell Jr.
Keyword(s):  
Surface Temperature ◽  
Linear Trend ◽  
Time Windows ◽  
Coupled Model ◽  
Global Scale ◽  
Polar Regions ◽  
Cmip5 Model ◽  
Model Simulations ◽  
Temperature Trends ◽  
North Polar

Abstract. Using the fifth Coupled Model Intercomparison Project (CMIP5) model simulations and two observational datasets, the surface temperature trends and their discrepancies have been examined. The temporal-spatial characteristics for the surface temperature trends are discussed. Different from a constant estimated linear trend for the entire simulation period of 1850–2012, a dynamical trend using running linear least squares fitting with the moving 10 yr time windows are calculated. The results show that the CMIP5 model simulations are generally in good agreement with the observational measurements for the global scale warming, but the temperature trends depend on the temporal change and the regional differences. Generally, contrary to the small discrepancies on the global scale, the large discrepancies are observed in the south- and north-polar regions and other sub-regions.

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