Moisture Anomalies Research Articles

Monitoring vegetation condition using microwave remote sensing: the standardized vegetation optical depth index (SVODI)

Abstract. Vegetation conditions can be monitored on a global scale using remote sensing observations in various wavelength domains. In the microwave domain, data from various spaceborne microwave missions are available from the late 1970s onwards. From these observations, vegetation optical depth (VOD) can be estimated, which is an indicator of the total canopy water content and hence of above-ground biomass and its moisture state. Observations of VOD anomalies would thus complement indicators based on visible and near-infrared observations, which are primarily an indicator of an ecosystem's photosynthetic activity. Reliable long-term vegetation state monitoring needs to account for the varying number of available observations over time caused by changes in the satellite constellation. To overcome this, we introduce the standardized vegetation optical depth index (SVODI), which is created by combining VOD estimates from multiple passive microwave sensors and frequencies. Different frequencies are sensitive to different parts of the vegetation canopy. Thus, combining them into a single index makes this index sensitive to deviations in any of the vegetation parts represented. SSM/I-, TMI-, AMSR-E-, WindSat- and AMSR2-derived C-, X- and Ku-band VODs are merged in a probabilistic manner resulting in a vegetation condition index spanning from 1987 to the present. SVODI shows similar temporal patterns to the well-established optical vegetation health index (VHI) derived from optical and thermal data. In regions where water availability is the main control on vegetation growth, SVODI also shows similar temporal patterns to the meteorological drought index scPDSI (self-calibrating Palmer drought severity index) and soil moisture anomalies from ERA5-Land. Temporal SVODI patterns relate to the climate oscillation indices SOI (Southern Oscillation index) and DMI (dipole mode index) in the relevant regions. It is further shown that anomalies occur in VHI and soil moisture anomalies before they occur in SVODI. The results demonstrate the potential of VOD to monitor the vegetation condition, supplementing existing optical indices. It comes with the advantages and disadvantages inherent to passive microwave remote sensing, such as being less susceptible to cloud coverage and solar illumination but at the cost of a lower spatial resolution. The index generation is not specific to VOD and could therefore find applications in other fields. The SVODI products (Moesinger et al., 2022) are open-access under Attribution 4.0 International and available at Zenodo, https://doi.org/10.5281/zenodo.7114654.

Boreal Summer Intraseasonal Meridional Oscillations Over East Asia and Their Influences on Precipitation

AbstractThe intraseasonal meridional wave trains are the major features of East Asia summer monsoon (EASM), but the complete physical pictures of dynamic pathways from tropical and mid‐high latitudes and their climate impacts have not been fully illustrated. With NCEP‐DOE Reanalysis‐II daily data set, two boreal summer intraseasonal meridional oscillations (ISMOs) are isolated and identified by extended EOF of zonal wind anomalies along the coast of East Asia on 10‐ to 30‐day timescale, that is, ISMO1 and ISMO2. They both demonstrate obvious quasi‐biweekly oscillations with an equivalent barotropic structure in troposphere, and ISMO1 represents the southward propagating wave train with large variances in upper level, which is initiated from the West Europe and primarily forced by the soil moisture anomalies over mid‐high latitudes of Eurasian continent, whereas ISMO2 propagates northward in mid‐lower troposphere from the tropical western North Pacific to East Asia that is closely related to BSISO2. The two ISMOs are capable of explaining a large fraction of the total intraseasonal variances of EASM, wherein, ISMO1 is responsible for a meridional dipole pattern of precipitation anomalies over eastern China, while ISMO2 has major influence in southern China. Generally, ISMO‐caused changes of extreme precipitation probability are in good accordance with the spatial pattern of its related 10‐ to 30‐day intraseasonal precipitation anomalies. Since ISMO1 and ISMO2 can clearly separate the tropical and extratropical‐origin meridional oscillations and further have robust influences on precipitation over East Asia, they can be used as the new monitoring and predicting factors to the intraseasonal variations of EASM and their associated extreme precipitation.

Joint Contribution of Preceding Pacific SST and Yunnan-Guizhou Plateau Soil Moisture to September Precipitation over the Middle Reaches of the Yellow River

The middle reaches of the Yellow River (MRYR) are an important base for agricultural and husbandry production and coal and coal-based power and chemical industries. Understanding the variability of autumn (especially September) precipitation over the MRYR region and the associated atmospheric circulation anomalies and precursory signals is of great importance for the prevention and mitigation of meteorological disasters during autumn rainy season. This study primarily explored precursory signals for September precipitation over the MRYR from the perspectives of sea surface temperature (SST) and soil moisture (SM) anomalies. The results reveal that the northward-shifted East Asian westerly jet (EAWJ) and the strengthened and westward-extended western Pacific subtropical high (WPSH) are responsible for more precipitation over the MRYR region. Further analyses show that the September MRYR precipitation is significantly related to the preceding July–August southern Pacific SST pattern (SPSP) and Yunnan-Guizhou Plateau (YGP) SM. The preceding SPSP anomaly, which reflects the La Niña/El Niño-like SST anomalies, can be maintained until September and plays an important role in modulating the September MRYR precipitation. Moreover, the above SST anomalies may adjust the SM anomalies in the YGP during July–August. The SM anomalies in The YGP persist from July–August to September and eventually affect the MRYR precipitation through exciting an anomalous vertical motion during September. The effect of the preceding SPSP anomaly on the September MRYR precipitation decreases when the SM effect is absent, which suggests that the YGP SM anomalies act as a bridge linking the preceding Pacific SST anomalies and the ensuing September MRYR precipitation. This study discloses the joint contribution of the preceding Pacific SST and YGP SM anomalies to the September MRYR precipitation and may shed new light on the short-term prediction of autumn precipitation over the MRYR.

Interactions between precipitation, evapotranspiration and soil-moisture-based indices to characterize drought with high-resolution remote sensing and land-surface model data

Abstract. The Iberian Peninsula is prone to drought due to the high variability in the Mediterranean climate with severe consequences for drinking water supply, agriculture, hydropower and ecosystem functioning. Because of the complexity and relevance of droughts in this region, it is necessary to increase our understanding of the temporal interactions of precipitation, evapotranspiration and soil moisture that originate from drought within the Ebro basin, in northeastern Spain, as the study region. Remote sensing and land-surface models provide high-spatial-resolution and high-temporal-resolution data to characterize evapotranspiration and soil moisture anomalies in detail. The increasing availability of these datasets has the potential to overcome the lack of in situ observations of evapotranspiration and soil moisture. In this study, remote sensing data of evapotranspiration from MOD16A2 and soil moisture data from SMOS1km as well as SURFEX-ISBA land-surface model data are used to calculate the evapotranspiration deficit index (ETDI) and the soil moisture deficit index (SMDI) for the period 2010–2017. The study compares the remote sensing time series of the ETDI and SMDI with the ones estimated using the land-surface model SURFEX-ISBA, including the standardized precipitation index (SPI) computed at a weekly scale. The study focuses on the analysis of the time lags between the indices to identify the synchronicity and memory of the anomalies between precipitation, evapotranspiration and soil moisture. Lag analysis results demonstrate the capabilities of the SPI, ETDI and SMDI drought indices computed at a weekly scale to give information about the mechanisms of drought propagation at distinct levels of the land–atmosphere system. Relevant feedback for both antecedent and subsequent conditions is identified, with a preeminent role of evapotranspiration in the link between rainfall and soil moisture. Both remote sensing and the land-surface model show capability to characterize drought events, with specific advantages and drawbacks of the remote sensing and land-surface model datasets. Results underline the value of analyzing drought with dedicated indices, preferably at a weekly scale, to better identify the quick self-intensifying and mitigating mechanisms governing drought, which are relevant for drought monitoring in semi-arid areas.

A two-step calibration framework for hydrological parameter regionalization based on streamflow and remote sensing evapotranspiration

• We proposed a two-step calibration based parameter regionalization method. • This method combines spatial proximity method and RS ET based parameter calibration method. • The proposed regionalization method achieves the overall best model performance. • The accuracy of RS ET is the main factor affecting the performance of the proposed method. Runoff prediction in ungauged catchments is an important and challenging research topic in the field of hydrology. However, traditional parameter regionalization methods may not be applicable in regions with sparsely distributed hydrological stations. Remote sensing (RS) evapotranspiration (ET) products provide valuable information for model parameter estimations. This study aimed to improve the accuracy of hydrological modeling in ungauged catchments by combining the traditional spatial proximity (SP) method with the RS ET-based parameter calibration method to form a two-step calibration-based regionalization method (SP-bcET method using bias-corrected RS ET data and SP-rawET method using raw RS ET data). The proposed regionalization scheme is demonstrated by using the variable infiltration capacity model in the Yangtze River Basin, China, and its performance was evaluated and compared with the SP method and two RS ET-based parameter calibration schemes (bcET method using bias-corrected RS ET data and rawET method using raw RS ET data). The results indicate that the bcET method performs better than the rawET method, the SP method outperforms the RS ET-based parameter calibration method, and the SP-bcET and SP-rawET methods achieve the best overall performance among the five methods. Taking the simulated streamflow at a daily scale in the calibration period as an example, the average Nash-Sutcliffe coefficient of efficiency for the SP, bcET, rawET, SP-bcET, and SP-rawET methods were 0.69, 0.61, 0.56, 0.71, and 0.71, respectively. Moreover, the SP-bcET and SP-rawET methods improved the simulation of the temporal dynamics and spatial variability of soil moisture anomalies. The accuracy of the RS ET data was one of the most dominant factors affecting the performance of the proposed parameter regionalization scheme. The SP-bcET method is expected to perform better than the SP method in warmer catchments or when gauged catchments are sparely distributed. The findings demonstrate the great potential of combining the traditional parameter regionalization and RS ET-based parameter calibration methods in hydrological simulations in ungauged catchments.

Characteristics of the Turkana low-level jet stream and the associated rainfall in CMIP6 models

The Turkana low-level jet stream (TJ) is important to climatic conditions over northern Kenya and East Africa. The representation of the TJ in climate models varies due to the TJ interaction with Turkana channel that is influenced by model resolution and influences the model representation of the regional climate. This study compares features of the TJ in CMIP6 AMIP model simulations with ERA5. Models reveal climatological wind speeds that match those of the reanalysis from the ERA5 at the jet entrance (13 m/s) but lower magnitudes of wind speed and vertical shears compared to ERA5 within the Turkana channel. The models with slowest wind speeds, have a flattened Turkana channel and fail to exhibit the terrain constriction at 37° E which otherwise aids in accelerating winds to form a jet core. Furthermore, they fail to represent the narrowing of the channel as in ERA5, thereby forming blocking walls in the channel, forcing vertical ascent and mixing, and weakening shear. This boosting of ascent motion promotes rainfall formation and enhances wet anomalies at the exit of the TJ when the jet stream is weaker. By applying a new narrowing index, we demonstrate the need to improve topography details in the CMIP6 models, particularly those with resolution coarser than 1.5°, in order to properly simulate the TJ and the observed rainfall over the northwestern areas of eastern Africa.

Synergy between satellite observations of soil moisture and water storage anomalies for runoff estimation

Abstract. This paper presents an innovative approach, STREAM – SaTellite-based Runoff Evaluation And Mapping – to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and total water storage anomalies (TWSAs). Within a very simple model structure, precipitation and soil moisture data are used to estimate the quick-flow river discharge component while TWSAs are used for obtaining its complementary part, i.e., the slow-flow river discharge component. The two are then added together to obtain river discharge estimates. The method is tested over the Mississippi River basin for the period 2003–2016 by using precipitation data from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), soil moisture data from the European Space Agency's Climate Change Initiative (ESA CCI), and total water storage data from the Gravity Recovery and Climate Experiment (GRACE). Despite the model simplicity, relatively high-performance scores are obtained in river discharge estimates, with a Kling–Gupta efficiency (KGE) index greater than 0.64 both at the basin outlet and over several inner stations used for model calibration, highlighting the high information content of satellite observations on surface processes. Potentially useful for multiple operational and scientific applications, from flood warning systems to the understanding of water cycle, the added value of the STREAM approach is twofold: (1) a simple modeling framework, potentially suitable for global runoff monitoring, at daily timescale when forced with satellite observations only, and (2) increased knowledge of natural processes and human activities as well as their interactions on the land.

Fast estimation and choice of confidence interval methods for step regression

In this paper we propose a new fast grid search algorithm for finding the least square estimators of a step regression model. This algorithm makes it practical to compute resampling-based confidence intervals for step regression models. We introduce five data generating models, including one where the mean model is a step model (model correctly specified) and four where the mean models are not step models (model misspecified), and use them to study the coverage probabilities of two new types of resampling-based confidence intervals for step regression: symmetric percentile bootstrap confidence intervals and subsampling confidence intervals using a new set of rules-of-thumb to select block size. Our results show that when the model is correctly specified, the symmetric percentile Efron bootstrap confidence intervals provide close-to-nominal coverage and have shorter intervals than the subsampling methods; when the model is misspecified, the subsampling method using the rules-of-thumb provides good coverage and shorter confidence intervals than the symmetric percentile Efron bootstrap method and the subsampling method using a double bootstrap-like procedure for block size selection. Finally, we apply the proposed methods to a real world environmental dataset on the relationship between grassland productivity, soil moisture anomalies and other hydro-climatic and land use variables to provide inference for the threshold in soil moisture anomalies, across which there is a jump in grassland productivity.

Dominant controls of cold-season precipitation variability over the high mountains of Asia

A robust understanding of the sub-seasonal cold season (November–March) precipitation variability over the High Mountains of Asia (HMA) is lacking. Here, we identify dynamic and thermodynamic pathways through which natural modes of climate variability establish their teleconnections over the HMA. First, we identify evaporative sources that contribute to the cold season precipitation over the HMA and surrounding areas. The predominant moisture contribution comes from the mid-latitude regions, including the Mediterranean/Caspian Seas and Mediterranean land. Second, we establish that several tropical and extratropical forcings display a sub-seasonally fluctuating influence on precipitation distribution over the region during the cold season. Many of them varyingly interact, so their impacts cannot be explained independently or at seasonal timescales. Lastly, a single set of evaporative sources is not identifiable as the key determinant in propagating a remote teleconnection because the sources of moisture anomalies depend on the pattern of sub-seasonally varying dynamical forcing in the atmosphere.

Application of real time S2S forecasts over Eastern Africa in the co-production of climate services

Application of real time S2S forecasts over Eastern Africa in the co-production of climate services

Mechanism of MJO-Modulated Triggering on the Rainy Season Onset over the Indian Subcontinent

Abstract As rainfed agriculture remains India’s critical source of livelihood, improving our understanding of rainy season onset timing in the region is of great importance for a better prediction. Using a new gridded dataset of rainy season characteristics, we found a clear phase relationship between the Madden–Julian oscillation (MJO) and the onset timing of the rainy season over the Indian subcontinent. A significantly high probability of rainy season onset is observed when the MJO convection stays over the western-central Indian Ocean. On the other hand, the rainy season onset is infrequent when the MJO is over the Maritime Continent and western Pacific. The MJO-associated convective instability with anomalous warm and moist air in the lower troposphere appears and grows during the period 10 days prior to the onset of rainy season, and drops substantially after the start of rainy season, suggesting its role as a trigger of rainy season onset. In contrast, the low-frequency background state (LFBS) with a period > 90 days favors a convectively unstable stratification even after the onset of the rainy season, supporting the succeeding precipitation during the entire rainy season. Based on the scale-decomposed moisture budget diagnosis, we further found that the key processes inducing the abrupt transition from a dry to a wet condition come mainly from two processes: 1) convergence of LFBS moisture by MJO-related circulation perturbations and 2) advection of MJO moisture anomalies by the background cross-equatorial flow toward the Indian subcontinent. The results may help provide a better and longer lead-time prediction of the rainy season onset over the Indian subcontinent.

A reversal in global occurrences of flash drought around 2000 identified by rapid changes in the standardized evaporative stress ratio

Flash drought is characterized by a rapid rate of onset and intensification within a few weeks. It usually accompanies exhausted soil moisture and high-temperature stress and exerts detrimental impacts on the growth of crops and the ecosystem. However, the global occurrence characteristics of flash drought in the recent four decades remain unclear. This study analyzes the spatiotemporal variability of flash drought identified by rapid decreases in the standardized evaporative stress ratio (SESR) from 1981 to 2020 and investigates their meteorological drivers. Results show that the flash drought mainly occurred in middle and low latitude areas. The coverage of flash drought showed a statistically significant decrease during 1981–2020. With the year of 2000 as a turning point, the coverage of flash drought trend reversed from a significant decline to a significant rise. Flash drought has no noticeable seasonal change. With the increase of the intensity of flash drought, the proportion of flash drought gradually decreased. Slight flash drought (FD_1; 50.9 %) is seven times of extreme flash drought (FD_4). The analysis of the evolution of hydro-meteorological variables concurrent with the global flash drought shows that flash drought was more triggered by abnormally low precipitation, soil moisture, evapotranspiration, and high temperature. In addition, the anomaly gradually increases with the increase of intensity. Water deficit is an important factor affecting the occurrence of flash drought, and only 10.9 % of flash drought events occurred in both positive soil moisture and precipitation anomalies. The results reference future research on flash drought on various spatial scales under a changing climate.

Irrigation characterization improved by the direct use of SMAP soil moisture anomalies within a data assimilation system

Prior soil moisture data assimilation (DA) efforts to incorporate human management features such as agricultural irrigation has only shown limited success. This is partly due to the fact that observational rescaling approaches for bias correction used in soil moisture DA systems are less effective when unmodeled processes such as irrigation are the dominant source of systematic biases. In this article, we demonstrate an alternative approach, i.e. anomaly correction for overcoming this limitation. Unlike the rescaling approaches, the proposed method does not scale remote sensing soil moisture retrievals to the model climatology, but it extracts the temporal variability information from the retrievals. The study demonstrates this approach through the assimilation of soil moisture retrievals from the Soil Moisture Active Passive mission into the Noah land surface model. The results demonstrate that DA using the anomaly correction method can better capture the effect of irrigation on soil moisture in agricultural areas while providing comparable performance to the DA integrations using rescaling approaches in non-irrigated areas. These findings emphasize the need to reduce inconsistencies between remote sensing and the models so that assimilation methods can employ information from remote sensing more directly to develop representations of unmodeled processes such as irrigation.

Characterizing natural variability in complex hydrological systems using passive microwave-based climate data records: a case study for the Okavango Delta

Abstract. The Okavango River system in southern Africa is known for its strong interannual variability of hydrological conditions. Here, we present how this is exposed in surface soil moisture, land surface temperature, and vegetation optical depth as derived from the Land Parameter Retrieval Model, using an inter-calibrated, long-term, multi-sensor passive microwave satellite data record (1998–2020). We also investigate how these interannual variations relate to state-of-the-art climate reanalysis data from ERA5-Land. We analysed both the upstream river catchment and the Okavango delta, supported by independent data records of discharge measurements, precipitation, and vegetation dynamics observed by optical satellites. The seasonal vegetation optical depth anomalies have a strong correspondence with the MODIS leaf area index (correlation catchment: 0.74, delta: 0.88). Land surface temperature anomalies derived from passive microwave observations match best with those of ERA5-Land (catchment: 0.88, delta: 0.81) as compared to MODIS nighttime land surface temperature (LST) (catchment: 0.70, delta: 0.65). Although surface soil moisture anomalies from passive microwave observations and ERA5-Land correlate reasonably well (catchment: 0.72, delta: 0.69), an in-depth evaluation over the delta uncovered situations where passive microwave satellites record strong fluctuations, while ERA5-Land does not. This is further analysed using information on inundated area, river discharge, and precipitation. The passive microwave soil moisture signal demonstrates a response to both the inundated area and precipitation. ERA5-Land however, which, by default, does not account for any lateral influx from rivers, only shows a response to the precipitation information that is used as forcing. This also causes the reanalysis model to miss record low land surface temperature values as it underestimates the latent heat flux in certain years. These findings demonstrate the complexity of this hydrological system and suggest that future land surface model generations should also include lateral land surface exchange. Also, our study highlights the importance of maintaining and improving climate data records of soil moisture, vegetation, and land surface temperature from passive microwave observations and other observation systems.

Interdecadal Variability of Spring Eurasian Snowmelt and Its Impact on Eastern China Summer Precipitation

In this study, the interdecadal variability of Eurasian spring snowmelt and its relationship with Eastern China summer precipitation (ECSP) are investigated based on observations and reanalysis data. Results show that the second mode of the empirical orthogonal function (EOF) analysis of the spring snowmelt, featured as a west-east dipole pattern, displays two interdecadal changes near the late-1970s and in the mid-2000s. The increased spring snowmelt over the Western Siberia (WSI) and the opposite situation over the Eastern Europe (EEU) are significantly linked to a meridional quadrupole summertime rainfall pattern on interdecadal time scales, with excessive rainfall over the regions of southern China (SC) and the Huang-Huai River (HHR) and deficient rainfall over the middle and lower reaches of Yangtze River Valley (YRV) and Inner Mongolia-northeastern China (IMNC). Besides, the possible mechanisms are discussed from the perspective of the hydrological effect related to snowmelt-induced soil moisture. Increased spring snowmelt can produce more water inflowing into the soil, leading to anomalous soil moisture, which can persist into summer. Excessive (deficient) snowmelt-related soil moisture anomalies over the WSI (EEU) in summer will change local land thermal conditions and thus generate cyclonic (anticyclonic) circulations with a quasi-barotropic structure. Therefore, the eastward-propagating wave-activity flux (WAF) could be strengthened over the WSI and EEU in the mid- and upper-troposphere and further propagate downstream. Combining the local response of atmospheric circulations to summer surface heating induced by local snowmelt-related SM anomalies over Mongolia, an anomalous anticyclonic (cyclonic) circulation near the Lake Baikal (SC) and the related strong descending (ascending) motion over the IMNC (SC) are founded. Besides, strong positive (negative) upper-level divergence appears over the YRV (HHR), which is the right (left) side of the exit region of upper-level jet. The upper-level divergence could be attributed to the convergence (divergence) of meridional winds on the right side of the anticyclonic circulation, thus the uniform descending (ascending) motion also appears over the YRV (HHR) through the collocation of upper-level convergence (divergence) and the compensatory lower-level circulations. Consequently, a quadrupole pattern of secondary circulation over Eastern China is founded, thereby causing the anomalously distributed ECSP under favorable water vapor conditions.

The Impact of Mean-State Moisture Biases on MJO Skill in the Navy ESPC

Abstract The Madden–Julian oscillation (MJO) is a key source of predictability in the subseasonal time scale (weeks to months) and influences a wide range of weather and climate phenomena. Although there have been enormous gains in simulating the MJO, many climate and forecast models still have biases in MJO behavior and structure. In this study, we examine the MJO in the Navy Earth System Prediction Capability (Navy ESPC) forecasts performed for the Subseasonal Experiment (SubX) using process-based diagnostics and a moisture budget analysis that uses wavenumber–frequency filtering to isolate the MJO. The MJO in the Navy ESPC is too strong in both boreal winter and summer. This amplitude bias is driven by biases in the vertical moisture advection in the Navy ESPC, which is too strong and deep, driven by a more bottom-heavy vertical motion profile and too steep lower-tropospheric vertical moisture gradient. Additionally, the convective moisture adjustment time scale in the Navy ESPC is faster than observed, such that for a given moisture anomaly the precipitation response is greater than observed. In the Navy ESPC, the MJO propagation shows strong agreement with observations in the Indian Ocean, followed by too rapid propagation east of the Maritime Continent in both seasons. This MJO acceleration east of the Maritime Continent is linked to an acceleration of moisture anomalies driven by biases in anomalous moisture tendency. The mechanisms that drive this bias have seasonal differences, with excess evaporation in the western Pacific dominating in boreal winter and horizontal moisture advection dominating in boreal summer.

Record-breaking dust loading during two mega dust storm events over northern China in March 2021: aerosol optical and radiative properties and meteorological drivers

Abstract. Although a remarkable reduction in the frequency of sand and dust storms (SDSs) in the past several decades has been reported over northern China (NC), two unexpected mega SDSs occurred on 15–20 and 27–29 March 2021 (abbreviated as the “3.15” and “3.27” SDS events), which has reawakened widespread concern. This study characterizes the optical, microphysical, and radiative properties of aerosols and their meteorological drivers during these two SDS events using the Sun photometer observations in Beijing and a comprehensive set of multiple satellite (including MODIS, VIIRS, CALIOP, and Himawari-8) and ground-based observations (including the CMA visibility network and AD-Net) combined with atmospheric reanalysis data. Moreover, a long-term (2000–2021) dust optical depth (DOD) dataset retrieved from MODIS measurements was also utilized to evaluate the historical ranking of the dust loading in NC during dust events. During the 3.15 and 3.27 events, the invasion of dust plumes greatly degraded the visibility over large areas of NC, with extreme low visibility of 50 and 500 m recorded at most sites on 15 and 28 March, respectively. Despite the shorter duration of the 3.27 event relative to the 3.15 event, sun photometer and satellite observations in Beijing recorded a larger peak AOD (∼2.5) in the former than in the latter (∼2.0), which was mainly attributed to the short-term intrusion of coarse-mode dust particles with larger effective radii (∼1.9 µm) and volume concentrations (∼2.0 µm3 µm−2) during the 3.27 event. The shortwave direct aerosol radiative forcing induced by dust was estimated to be −92.1 and −111.4 W m−2 at the top of the atmosphere, −184.7 and −296.2 W m−2 at the surface, and +92.6 and +184.8 W m−2 in the atmosphere in Beijing during the 3.15 and 3.27 events, respectively. CALIOP observations show that during the 3.15 event the dust plume was lifted to an altitude of 4–8 km, and its range of impact extended from the dust source to the eastern coast of China. In contrast, the lifting height of the dust plume during the 3.27 event was lower than that during the 3.15 event, which was also confirmed by ground-based lidar observations. The MODIS-retrieved DOD data registered these two massive SDS events as the most intense episode in the same period in history over the past 2 decades. These two extreme SDS events were associated with both atmospheric circulation extremes and local meteorological anomalies that favored enhanced dust emissions in the Gobi Desert (GD) across southern Mongolia and NC. Meteorological analysis revealed that both SDS events were triggered by an exceptionally strong Mongolian cyclone generated at nearly the same location (along the central and eastern plateau of Inner Mongolia) in conjunction with a surface-level cold high-pressure system at the rear, albeit with differences in magnitude and spatial extent of impact. In the GD, the early melting of spring snow caused by near-surface temperature anomalies over dust source regions, together with negative soil moisture anomalies induced by decreased precipitation, formed drier and barer soil surfaces, which allowed for increased emissions of dust into the atmosphere by strongly enhanced surface winds generated by the Mongolian cyclone.

Relative Contributions of Sea Surface Temperature and Atmospheric Nonlinearities to ENSO Asymmetrical Rainfall Response

Abstract Here, we investigate the relative roles of atmospheric nonlinearities and asymmetrical sea surface temperature (SST) forcing in the El Niño–Southern Oscillation (ENSO) asymmetrical rainfall response. Applying a vertically integrated water vapor budget to the ERA5 reanalysis leads to a simple analytical equation for precipitation anomalies. This formulation reveals that ENSO rainfall anomalies are dominated by the linear component of the dynamical term (i.e., the anomalous moisture convergence due to the effect of circulation anomalies on climatological humidity). Nonlinearities in this term and the linear thermodynamical term (i.e., the effect of climatological circulation on humidity anomalies) both strengthen central Pacific rainfall anomalies for both ENSO phases. In contrast, the nonlinear term associated with the effect of anomalous divergence on anomalous moisture (i.e., the mixed term) weakens La Niña dry and strengthens El Niño wet anomalies, in particular during extreme El Niño events when it contributes to about 40% of the eastern Pacific wet anomalies. Overall, atmospheric nonlinearities directly account for ∼70% of the positively skewed ENSO rainfall distribution east of the date line, and ∼50% of the negatively skewed rainfall distribution in the western Pacific. The remaining ENSO rainfall asymmetries are attributable to the asymmetrical ENSO SST pattern. This asymmetrical SST pattern also has contributions from atmospheric nonlinearities through the Bjerknes feedback loop, in addition to those from oceanic nonlinearities. Our estimates are thus likely a lower bound of the contribution of atmospheric nonlinearities to the overall ENSO rainfall asymmetry.

Deficient precipitation sensitivity to Sahel land surface forcings among CMIP5 models

AbstractThe overall performance of the simulated seasonal precipitation response to local terrestrial forcings, namely vegetation abundance and soil moisture, in the Sahel among the Coupled Model Intercomparison Project Phase Five (CMIP5) Earth System Models (ESMs) is systematically investigated and compared with its observational counterpart using a multivariate statistical method. The observed seasonal precipitation response is evaluated against a large ensemble of observational, reanalysis, and satellite data sets to provide quantification of uncertainties. The behaviour of models with and without a Dynamic Global Vegetation Model (DGVM) component is also explored, along with the mechanisms responsible for terrestrial feedback on rainfall. In general, the CMIP5 models can reasonably capture the seasonal evolution of Sahel precipitation and soil moisture, albeit with wet biases during the pre‐monsoon period and dry biases during the peak monsoon period. The non‐DGVM ESMs simulate comparable leaf area indices (LAIs) with observations, while DGVM‐enabled ESMs simulate too much year‐round LAI. The variance of precipitation that is attributed to oceanic forcings in CMIP5 is comparable with observations; however, the variance of precipitation that is attributed to terrestrial forcings is smaller in CMIP5 models than observed, especially for non‐DGVM ESMs. CMIP5 models, especially those without DGVMs, undervalue precipitation's observed response strength to soil moisture anomalies. In both observations and CMIP5 models, none of the atmospheric variables show significant responses to direct vegetation forcing, except for the response in transpiration. Although vegetation has minimal direct effect on the atmospheric state, it can affect the atmosphere by modifying soil moisture and transpiration rate indirectly, which helps explain the more realistic simulation of rainfall in DGVM‐enabled ESMs than non‐DGVM ESMs. Coupling of an ESM to a DGVM is critical in generating reasonable land–atmosphere feedback and examining future ecological and climatic changes over the Sahel.

The Seasonal Evolution of the Tibetan Plateau Snow Cover Related Moisture During Spring‐to‐Summer

AbstractThe current work investigated the seasonal evolution of the Tibetan Plateau (TP) snow cover (TPSC) induced atmospheric moisture anomalies from May to August, by analyzing TP snow‐diabatic and TP snow‐hydrological processes during the 1979–2020 period using both observational data and a baroclinic atmospheric model. The results show that the TPSC and its related atmospheric moisture are featured by a westward retreat from the eastern TP in May to the western TP in August. The TPSC variation related atmospheric apparent heat source shows that excessive TPSC can cause local diabatic cooling to the above atmosphere and can be attributed to a local cyclonic pattern in the upper troposphere. This cyclonic pattern also moves westward along with the seasonal evolution of the TP snow during this period. Anomalous TP snow can impact atmospheric moisture through snow melt and snow evaporating process. Associated with the changes in TP snow, its related anomalous atmospheric moisture and circulations, significant moisture divergence appears over the mid‐eastern Himalaya during May and June (MJ) and the water vapor is mainly transported from the Himalaya to southeastern TP regions including Bhutan, Myanmar, Bengal and eastern India. Differently, during July and August (JA), significant atmospheric moisture divergence is located over the mid‐western Himalaya, and the water vapor is mostly transported from the Himalaya to southwestern TP areas including Nepal and western India. Further analysis shows that the TPSC anomaly induced anomalous atmospheric moisture accounts for 22%–45% of the climatological moisture for the mid‐eastern Himalaya during MJ, whereas it accounts for 65%–78% of the climatological moisture for the mid‐western Himalaya during JA.