Multiple Observational Datasets Research Articles

Synchronization of the Recent Decline of East African Long Rains and Northwestern Eurasian Warming

AbstractThe East African March–April–May (MAM, “long rains”) precipitation decline in recent decades remains a puzzle marked by various proposed large‐scale drivers. Here, the interannual variability of the long rains and their recent drying trend are examined using global model simulations and observations. Comparison of a control simulation and re‐initialized simulations in which land‐surface feedback is suppressed shows that much of the long rains deficit experienced between 1980 and 2014 is synchronized with the warming of the Northwestern Eurasian landmass. In agreement with the modeling results, multiple observational data sets reveal a strong negative correlation between MAM mean East African rainfall amount and the surface temperature over Northwestern Eurasia. Idealized simulations further indicate that warming in Northwestern Eurasia weakens the regional Hadley Cell and diverts the monsoonal transport of moisture away from Eastern Africa toward Europe and southern Africa, highlighting the role of remote land surface warming on the observed precipitation decline.

A Physical‐Informed Neural Network for Improving Air‐Sea Turbulent Heat Flux Parameterization

AbstractThe parameterizations of air‐sea turbulent heat flux are one of the major bottlenecks in atmosphere‐ocean coupled model development, which play a crucial role in sea surface temperature (SST) prediction. Recently, neural networks start to be applied for the development of parameterizations of interface turbulent heat flux. However, these new parameterizations are primairily developed for specific regions and have not been tested in real atmosphere‐ocean coupled models. In this study, we propose a new air‐sea heat flux parameterization using a physical‐informed neural network (PINN) based on multiple observational data sets worldwide. Evaluated with an independent observation data set, it is shown that the PINN can significantly reduce the RMSE of latent heat flux by at least about 48.6% compared to three traditional bulk formulas. Moreover, the PINN can be flexibly updated with new observational data by transfer learning. To test the performance of the new parameterization in realistic application, we implement the PINN into a global ocean‐atmosphere coupled model and make seasonal forecasts for the first time. The PINN markedly reduce the errors of equatorial SST forecast, indicating a good performance of the PINN‐based air‐sea turbulent heat flux scheme. Noticeably, due to limited observational data, the NN‐based parameterizations tend to underestimate heat flux at high wind speeds compared with bulk formula‐based parameterizations. With more data available at extreme conditions, the PINN can be improved via transfer learning and need to be futher evaluated. This study suggests that PINN‐based air‐sea heat flux parameterization is promising to improve SST simulation.

Elevation-dependent biases of raw and bias-adjusted EURO-CORDEX regional climate models in the European Alps

Data from the EURO-CORDEX ensemble of regional climate model simulations and the CORDEX-Adjust dataset were evaluated over the European Alps using multiple gridded observational datasets. Biases, which are here defined as the difference between models and observations, were assessed as a function of the elevation for different climate indices that span average and extreme conditions. Moreover, we assessed the impact of different observational datasets on the evaluation, including E-OBS, APGD, and high-resolution national datasets. Furthermore, we assessed the bi-variate dependency of temperature and precipitation biases, their temporal evolution, and the impact of different bias adjustment methods and bias adjustment reference datasets. Biases in seasonal temperature, seasonal precipitation, and wet-day frequency were found to increase with elevation. Differences in temporal trends between RCMs and observations caused a temporal dependency of biases, which could be removed by detrending both observations and RCMs. The choice of the reference observation datasets used for bias adjustment turned out to be more relevant than the choice of the bias adjustment method itself. Consequently, climate change assessments in mountain regions need to pay particular attention to the choice of observational dataset and, furthermore, to the elevation dependence of biases and the increasing observational uncertainty with elevation in order to provide robust information on future climate.

Response of salt intrusion in a tidal estuary to regional climatic forcing

Salinity distribution in a large tidal estuary is subject to estuarine adjustment under the influences of multiple physical drivers such as freshwater pulses and sea level rise, and is crucial to upstream water quality, aquaculture, and ecosystem functions of the estuary. To better understand the estuarine salinity response to climate change, the unstructured-grid Finite Volume Community Ocean Model was implemented to simulate the salt intrusion in the Delaware Bay Estuary. The model was first validated by multiple observational data sets and subsequently applied in an idealized setting to examine the response of salt front to freshwater pulses in high flow conditions, followed by a long-term drought condition supported by a multi-decadal streamflow drought analysis in the estuary. The model results showed that after the freshwater pulses the salt front location moved further upstream with sea level rise. Under the simulated long-term drought condition, the adjustment timescale of salt intrusion varies nonlinearly with sea level rise. With a significant increase in sea level rise, the adjustment timescale starts to decrease. This shift suggests a transition into a different regime where the estuary becomes more stratified, as indicated by an increasing bulk Simpson number with rising sea levels.

Ocean Heat Convergence and North Atlantic multidecadal heat content variability

Abstract We construct an upper ocean (0-1000m) North Atlantic heat budget (26°-67°N) for the period 1950-2020 using multiple observational datasets and an eddy-permitting global ocean model. On multidecadal timescales ocean heat transport convergence controls ocean heat content (OHC) tendency in most regions of the North Atlantic with little role for diffusive processes. In the subpolar North Atlantic (45°N-67°N) heat transport convergence is explained by geostrophic currents whereas ageostrophic currents make a significant contribution in the subtropics (26°N-45°N). The geostrophic contribution in all regions is dominated by anomalous advection across the time-mean temperature gradient although other processes make a significant contribution particularly in the subtropics. The timescale and spatial distribution of the anomalous geostrophic currents are consistent with a simple model of basin scale thermal Rossby waves propagating westwards/northwestwards in the subpolar gyre and multidecadal variations in regional OHC are explained by geostrophic currents periodically coming into alignment with the mean temperature gradient as the Rossby wave passes through. The global ocean model simulation shows that multidecadal variations in the Atlantic Meridional Overturning Circulation are synchronized with the ocean heat transport convergence consistent with modulation of the west-east pressure gradient by the propagating Rossby wave.

Precipitation Seasonality Amplifies as Earth Warms

AbstractPrecipitation exhibits a pronounced seasonal cycle, of which the phase and amplitude are closely associated with water resource management. While previous studies suggested an emerged delaying phase in the past decades, whether the amplified amplitude has emerged is controversial. Using multiple observational data sets and climate simulations, here we show that the amplification of precipitation annual cycle has emerged in most global land areas since the 1980s, especially in the tropics. These amplifications are mainly driven by anthropogenic emissions, and will be further intensified by 17.6% in the future (2081–2100) under high emission scenario (Shared Socioeconomic Pathways, SSP585), and limited to 7.2% under SSP126 scenario, relative to the historical period (1982–2014). Precipitation seasonality will be amplified by 4.2% for each 1°C of global warming, which is seen in all emission scenarios. The mitigation of lower emissions is helpful for alleviating the amplification of precipitation seasonality in a warming world.

Quantifying Contributions of External Forcing and Internal Variability to Arctic Warming During 1900–2021

AbstractArctic warming has significant environmental and social impacts. Arctic long‐term warming trend is modulated by decadal‐to‐multidecadal variations. Improved understanding of how different external forcings and internal variability affect Arctic surface air temperature (SAT) is crucial for explaining and predicting Arctic climate changes. We analyze multiple observational data sets and large ensembles of climate model simulations to quantify the contributions of specific external forcings and various modes of internal variability to Arctic SAT changes during 1900–2021. We find that the long‐term trend and total variance in Arctic‐mean SAT since 1900 are largely forced responses, including warming due to greenhouse gases and natural forcings and cooling due to anthropogenic aerosols. In contrast, internal variability dominates the early 20th century Arctic warming and mid‐20th century Arctic cooling. Internal variability also explains ∼40% of the recent Arctic warming from 1979 to 2021. Unforced changes in Arctic SAT are largely attributed to two leading modes. The first is pan‐Arctic warming with stronger loading over the Eurasian sector, accounting for 70% of the unforced variance and closely related to the positive phase of the unforced Atlantic Multidecadal Oscillation (AMO). The second mode exhibits relatively weak warming averaged over the entire Arctic with warming over the North American‐Pacific sector and cooling over the Atlantic sector, explaining 10% of the unforced variance and likely caused by the positive phase of the unforced Interdecadal Pacific Oscillation (IPO). The AMO‐related changes dominate the unforced Arctic warming since 1979, while the IPO‐related changes contribute to the decadal SAT changes over the North American‐Pacific Arctic.

Fusion of biomedical imaging studies for increased sample size and diversity: a case study of brain MRI.

Data collection, curation, and cleaning constitute a crucial phase in Machine Learning (ML) projects. In biomedical ML, it is often desirable to leverage multiple datasets to increase sample size and diversity, but this poses unique challenges, which arise from heterogeneity in study design, data descriptors, file system organization, and metadata. In this study, we present an approach to the integration of multiple brain MRI datasets with a focus on homogenization of their organization and preprocessing for ML. We use our own fusion example (approximately 84,000 images from 54,000 subjects, 12 studies, and 88 individual scanners) to illustrate and discuss the issues faced by study fusion efforts, and we examine key decisions necessary during dataset homogenization, presenting in detail a database structure flexible enough to accommodate multiple observational MRI datasets. We believe our approach can provide a basis for future similarly-minded biomedical ML projects.

Improving Earth System Model Selection Methodologies for Projecting Hydroclimatic Change: Case Study in the Pacific Northwest

AbstractThe rapid expansion of Earth system model (ESM) data available from the Coupled Model Intercomparison Project Phase 6 (CMIP6) necessitates new methods to evaluate the performance and suitability of ESMs used for hydroclimate applications as these extremely large data volumes complicate stakeholder efforts to use new ESM outputs in updated climate vulnerability and impact assessments. We develop an analysis framework to inform ESM sub‐selection based on process‐oriented considerations and demonstrate its performance for a regional application in the US Pacific Northwest. First, a suite of global and regional metrics is calculated, using multiple historical observation datasets to assess ESM performance. These metrics are then used to rank CMIP6 models, and a culled ensemble of models is selected using a trend‐related diagnostics approach. This culling strategy does not dramatically change climate scenario trend projections in this region, despite retaining only 20% of the CMIP6 ESMs in the final model ensemble. The reliability of the culled trend projection envelope and model response similarity is also assessed using a perfect model framework. The absolute difference in temperature trend projections is reduced relative to the full ensemble compared to the model for each SSP scenario, while precipitation trend errors are largely unaffected. In addition, we find that the spread of the culled ensemble temperature and precipitation trends includes the trend of the “truth” model ∼83%‐92% of the time. This analysis demonstrates a reliable method to reduce ESM ensemble size that can ease use of ESMs for creating and understanding climate vulnerability and impact assessments.

Evaluation and Bias Correction of the ERA5 Reanalysis over the United States for Wind and Solar Energy Applications

The applicability of the ERA5 reanalysis for estimating wind and solar energy generation over the contiguous United States is evaluated using wind speed and irradiance variables from multiple observational data sets. After converting ERA5 and observed meteorological variables into wind power and solar power, comparisons demonstrate that significant errors in the ERA5 reanalysis exist that limit its direct applicability for a wind and solar energy analysis. Overall, ERA5-derived solar power is biased high, while ERA5-derived wind power is biased low. During winter, the ERA5-derived solar power is biased high by 23% on average, while on an annual basis, the ERA5-derived wind power is biased low by 20%. ERA5-derived solar power errors are found to have consistent characteristics across the contiguous United States. Errors for the shortest duration and most extreme solar negative anomaly events are relatively small in the ERA5 when completely overcast conditions occur in both the ERA5 and observations. However, longer-duration anomaly events on weekly to monthly timescales, which include partially cloudy days or a mix of cloudy and sunny days, have significant ERA5 errors. At 10 days duration, the ERA5-derived average solar power produced during the largest negative anomaly events is 62% greater than observed. The ERA5 wind speed and derived wind power negative biases are largely consistent across the central and northwestern U.S., and offshore, while the northeastern U.S. has an overall small net bias. For the ERA5-derived most extreme negative anomaly wind power events, at some sites at 10 days duration, the ERA5-derived wind power produced can be less than half of that observed. Corrections to ERA5 are derived using a quantile–quantile method for solar power and linear regression of wind speed for wind power. These methods are shown to avoid potential over-inflation of the reanalysis variability resulting from differences between point measurements and the temporally and spatially smoother reanalysis values. The corrections greatly reduce the ERA5 errors, including those for extreme events associated with wind and solar energy droughts, which will be most challenging for electric grid operation.

Robust Weakening of the Gulf Stream During the Past Four Decades Observed in the Florida Straits

AbstractThe Gulf Stream is a vital limb of the North Atlantic circulation that influences regional climate, sea level, and hurricane activity. Given the Gulf Stream's relevance to weather and climate, many studies have attempted to estimate trends in its volumetric transport from various data sets, but results have been inconclusive, and no consensus has emerged whether it is weakening with climate change. Here we use Bayesian analysis to jointly assimilate multiple observational data sets from the Florida Straits to quantify uncertainty and change in Gulf Stream volume transport since 1982. We find with virtual certainty (probability P > 99%) that Gulf Stream volume transport through the Florida Straits declined by 1.2 ± 1.0 Sv in the past 40 years (95% credible interval). This significant trend has emerged from the data set only over the past ten years, the first unequivocal evidence for a recent multidecadal decline in this climate‐relevant component of ocean circulation.

Quantifying the Relationship between Embedded Rotation and Extreme Rainfall Rates in Observations of Tropical Storm Imelda (2019)

Abstract Previous work on continental convective systems has indicated that there is a positive relationship between short-term rainfall rates and storm-scale to mesoscale rotation. However, little has been done to explore this relationship in dense observing networks or in landfalling tropical cyclone (LTC) environments. In an effort to quantify the relationship between rainfall rates and embedded rotation of this scale, we use several sets of observations that were collected during Tropical Storm Imelda (2019). First, a meteorological overview of the event is presented, and the ingredients that led to its flash flood–producing rainfall are discussed. Then, two analyses that investigate the relationship between rainfall rates and storm-scale to mesoscale rotation in the LTC remnants are examined. The first method relies on products from the Multi-Radar Multi-Sensor system, where two spatial averaging approaches are applied to the 0–2-km accumulated rotation track and gauge bias-corrected quantitative precipitation estimate products over hourly time periods. Using these fields as proxies for rotation and rain rates, the results show a positive spatiotemporal relationship between the two products. The second method time matches subjectively identified radar-based rotation and 5-min surface rain gauge observations. There, we show that nearly twice the amount of rain was recorded by the gauges when storm-scale to mesoscale rotation was present nearby, and the differences in 5-min rainfall observations between when rotation was present versus not was statistically significant. Together, these results indicate that more rain tended to fall in locations where there was rotation embedded in the system. Significance Statement Tornadoes and flash floods frequently occur in unison over the same locations, which can complicate forecasting, warning, and communication efforts within the meteorology community. Previous work has furthered the understanding of the interconnectedness of these hazards by suggesting a relationship between two of their predecessors: storm rotation and rainfall rates. We build on this research by quantifying the relationship of these two processes using observations from Tropical Storm Imelda: a system that brought devastating flooding to southeast Texas in September 2019. Our results show across multiple observational datasets that more rain tended to fall in locations where there was rotation embedded in the tropical storm remnants.

Large-scale climatic drivers for warm-season compound drought and heatwave frequency over North China

Based on multiple observational and reanalysis datasets, this study investigated the large-scale climatic drivers responsible for the warm season (April to September) compound drought and heatwave (CDHW) frequency over the North China (NC) domain, both locally and remotely. The warm-season NC CDHW frequency exhibited salient year-to-year variation. We identified that the extensive equivalent barotropic high-pressure anomaly centered around Lake Baikal could be the predominant local-scale driving factor. In addition, the low-pressure circulation anomaly to the southeast played a secondary dynamic amplification role. Such a dipole pattern can jointly cause more frequent occurrences of NC CDHWs by triggering anomalies in localized meteorological variables (e.g., surface warming and drying) and land surface variable (reduced soil moisture) that are favorable for CDHW formation during the warm season. Regarding remote driving factors, central eastern Maritime Continent (MC) convection centered over New Guinea could be an influential and direct atmospheric forcing factor. Meanwhile, the sea surface temperature (SST) anomaly pattern over the tropical eastern Pacific (TEP) was identified as a crucial oceanic forcing indirectly modulating the NC CDHW frequency through the critical medium of MC convection. Through a Hadley-like meridional overturning circulation pattern, the suppressed MC convection can remotely induce a dipolar pattern similar to that related to a higher NC CDHW frequency, exerting a so-called direct modulation effect. Furthermore, positive TEP SST anomalies act as anomalous Rossby wave sources via anomalous upper-tropospheric divergence, characterized by westward-propagating descending Rossby waves with notable wave energy advecting westward toward the central–eastern MC. In such a scenario, a reversed Walker-like circulation pattern can form to sustain the suppressed MC convection, playing an intermediate role in the maintenance of the high-pressure anomaly centered around Lake Baikal. Our findings may provide new insights into the synergistic roles of distant climatic driving factors in the modulation and maintenance of interannual variations of warm-season NC CDHW.

Flow regime changes in the Lancang River, revealed by integrated modeling with multiple Earth observation datasets

The flow regime change of rivers, especially transboundary rivers, affected by reservoir regulations is evident worldwide and has received much attention. Investigating dam-induced flow regime alterations is essential for understanding potential adverse downstream effects and facilitating dialogue around coordinated water use in transboundary basins, such as the Lancang River Basin (LRB). This study explored the value of combining several types of satellite Earth observation (EO) datasets that monitor different water balance components to constrain the parameter space of lumped conceptual hydrological models. Thus, we aimed to reconstruct the natural flow regimes upstream and downstream of the cascade reservoirs. Specifically, reservoir water storage changes were first estimated using satellite imagery and altimetry datasets. Then, storage changes were combined with hydrological model simulations of reservoir inflow to estimate the regulated flow regime downstream. Our results showed that integrated hydrological modeling combined with EO datasets exhibited better overall performance. Continuous warming and drying of the LRB resulted in a decrease in discharge of approximately 47 %. By comparing the simulated natural and regulated flow regimes, we revealed the pivotal role of the Xiaowan and Nuozhadu reservoirs in regulating natural flows. The wet season shortens (approximately 45 days), the flood peak flattens, and the low flow in the dry season has primarily increases. The two reservoirs attenuated 50 % of the flood peaks in the wet seasons and mitigated droughts by releasing up to 100 % of the natural flows in the dry seasons at the China–Laos border. Overall, these results enhance the understanding of upper reservoir operation, and the approaches can be applied to studies of dammed basins under climate change scenarios when knowledge of the upstream area is limited.

An improved estimate of soil carbon pool and carbon fluxes in the Qinghai-Tibetan grasslands using data assimilation with an ecosystem biogeochemical model

The accurate estimation of soil carbon (C) pool and fluxes is a prerequisite to better understand the terrestrial C feedback to climate change. However, recent studies showed considerable uncertainties in soil C estimates. To provide a reliable C estimate in the grasslands of the Qinghai-Tibet Plateau (QTP), we calibrated key parameters in a process-based ecosystem model (the CENTURY model) through data assimilation based on 570 soil samples and 21 sites of eddy covariance measurements. Two assimilating strategies (Opt1 – assimilating C pool observations; Opt2 –assimilating both C pool and C flux) were examined. Compared to default parameterization, our results showed both Opt1 and Opt2 improved the soil organic carbon density (SOCD) estimation, with R2 increasing from 0.59 to 0.75 and 0.73, respectively. Opt2 was superior to Opt1 in constraint of parameters dominating aboveground processes and yield a better estimation of net ecosystem production (NEP). Based on different parameterization, the spatial variability of SOCD and NEP across the QTP grassland were generated. Both Opt1 and Opt2 ameliorated the overestimation of SOCD by the default model, estimating a total soil C of 6.63 Pg and 6.48 Pg C for the topsoil (0–30 cm) of the QTP grasslands, respectively. Opt2 showed lower uncertainties in the NEP estimation and predicted a net sink of 14.33 Tg C annually. Compared with existing datasets, our study provided a more reliable estimation of carbon storage and fluxes in the QTP grassland with the calibrated ecosystem model. The results highlight that data assimilation with multiple observational data sets is promising to constrain process-based ecosystem models and increase the robustness of model predictions for terrestrial C cycle feedback to future climate change.

Anthropogenic Influence on the Diurnal Temperature Range since 1901

Abstract The diurnal temperature range (DTR) as measured by the difference between daily maximum (Tmax) and minimum (Tmin) temperatures is of great importance to human health, ecology, and agriculture. The link of its long-term change to anthropogenic forcing is still unclear. This study shows evidence of human influence on long-term changes in DTR over the globe, five continents, and China during the past century (1901–2014). Using multiple observational datasets, we find a general decrease in the DTR over most of the global land since 1901, especially after the mid-1950s. Changes in DTR are due to different warming rates of Tmax and Tmin in response to external forcings. The climate models that participated in phase 6 of the Coupled Model Intercomparison Project Phase 6 (CMIP6) generally reproduce most of the changes in DTR, along with those in Tmax and Tmin. The models have underestimated the observed changes in DTR, however. A formal detection and attribution analysis shows that the anthropogenic forcing signal, including both greenhouse gas and aerosol emissions but dominated by the greenhouse gas emissions, is the main driver for these changes. The anthropogenic aerosol signal can be detected in Tmax and Tmin but not in DTR during the period of 1901–2014 over the globe and most continents. These indicate the observed decrease in DTR is not a simple response to anthropogenic aerosol emission. The natural signal is negligible in almost all the cases. Globally, anthropogenic influence is estimated to explain more than 90% of the observed changes in the three variables. In China, human influence is also clearly detected, although model simulated results on the regional scale have larger deviation. Significance Statement The diurnal temperature range (DTR) is of great importance in many areas. We compare multiple observational datasets with the simulations by climate models that participated in the latest phase (phase 6) of the Coupled Model Intercomparison Project (CMIP6), finding evidence of human influence on long-term changes in DTR over the past century (1901–2014) and robust evidence for the period since the early 1950s. The decrease in DTR as seen in the observational dataset is caused by different warming rates of daily maximum and daily minimum temperature in response to anthropogenic forcing, including both greenhouse gases and aerosols.

Sustained Water Storage in Horn of Africa Drylands Dominated by Seasonal Rainfall Extremes

AbstractRural communities in the Horn of Africa Drylands (HADs) are increasingly vulnerable to multi‐season droughts due to the strong dependence of livelihoods on seasonal rainfall. We analyzed multiple observational rainfall data sets for recent decadal trends in mean and extreme seasonal rainfall, as well as satellite‐derived terrestrial water storage and soil moisture trends arising from two key rainfall seasons across various subregions of HAD. We show that, despite decreases in total March‐April‐May rainfall, total water storage in the HAD has increased. This trend correlates strongly with seasonal totals and especially with extreme rainfall in the two dominant HAD rainy seasons between 2003 and 2016. We further show that high‐intensity October‐November‐December rainfall associated with positive Indian Ocean Dipole events lead to the largest seasonal increases in water storage that persist over multiple years. These findings suggest that developing groundwater resources in HAD could offset or mitigate the impacts of increasingly common droughts.

Greenhouse Gas Emissions Drive Global Dryland Expansion but Not Spatial Patterns of Change in Aridification

Abstract Drylands play an essential role in Earth’s environment and human systems. Although dryland expansion has been widely investigated in previous studies, there is a lack of quantitative evidence supporting human-induced changes in dryland extent. Here, using multiple observational datasets and model simulations from phase 6 of the Coupled Model Intercomparison Project, we employ both correlation-based and optimal fingerprinting approaches to conduct quantitative detection and attribution of dryland expansion. Our results show that spatial changes in atmospheric aridity (i.e., the aridity index defined by the ratio of precipitation to potential evapotranspiration) between the recent period 1990–2014 and the past period 1950–74 are unlikely to have been caused by greenhouse gas (GHG) emissions. However, it is very likely (at least 95% confidence level) that dryland expansion at the global scale was driven principally by GHG emissions. Over the period 1950–2014, global drylands expanded by 3.67% according to observations, and the dryland expansion attributed to GHG emissions is estimated as ∼4.5%. Drylands are projected to continue expanding, and their populations to increase until global warming reaches ∼3.5°C above preindustrial temperature under the middle- and high-emission scenarios. If warming exceeds ∼3.5°C, a reduction in population density would drive a decrease in dryland population. Our results for the first time provide quantitative evidence for the dominant effects of GHG emissions on global dryland expansion, which is helpful for anthropogenic climate change adaptation in drylands. Significance Statement In the past decades, global drylands have been reported to show changes in space and time, based on atmospheric aridity (i.e., aridity index defined by the ratio of precipitation to potential evapotranspiration). Using two detection and attribution methods, the spatial change patterns of atmospheric aridity between 1990–2014 and 1950–74 are unlikely to be driven by greenhouse gas (GHG) emissions, whereas the temporal expansion of global drylands (i.e., 3.67% from 1950 to 2014) is principally attributed to GHG emissions (contribution: ∼122%). Quantitative evidence from the detection and attribution analysis supports the dominant role of greenhouse gas emissions in global dryland expansion, which will increase the population suffering from water shortages under future warming unless climate adaptation is adopted.

Abrupt emission reduction during COVID-19 intensified the spring 2020 rainfall over India

The high level of aerosol pollution in South Asia has a measurable impact on clouds, radiation, and precipitation. Here, exploring multiple observational data sets and simulations of the state-of-the-art ECHAM6-HAMMOZ chemistry-climate model, we report that the reduction in anthropogenic emissions during the COVID-19 lockdown period has enhanced precipitation by 5–25% over India. This precipitation enhancement is the result of the combined effect of an enhancement in cloud cover, a reduction in aerosol induced cloud invigoration and dynamical changes. We observed that the increase in cloud cover was associated with a reduction in cloud base height and an increase in the effective radius of cloud particles which led to an increase in cloud water content. In response to sudden emission reduction, an anomalous northward moisture transport was observed adding convection and precipitation over the Indian region. Importantly, we show that there is an advantage of anthropogenic pollution reduction for water availability in addition to benefits of air quality, human health, and crop yield.

Increasing Inhomogeneity of the Global Oceans

AbstractThe ocean is inhomogeneous in hydrographic properties with diverse water masses. Yet, how this inhomogeneity has evolved in a rapidly changing climate has not been investigated. Using multiple observational and reanalysis datasets, we show that the spatial standard deviation (SSD) of the global ocean has increased by 1.4 ± 0.1% in temperature and 1.5 ± 0.1% in salinity since 1960. A newly defined thermohaline inhomogeneity index, a holistic measure of both temperature and salinity changes, has increased by 2.4 ± 0.1%. Climate model simulations suggest that the observed ocean inhomogeneity increase is dominated by anthropogenic forcing and projected to accelerate by 200%–300% during 2015–2100. Geographically, the rapid upper‐ocean warming at mid‐to‐low latitudes dominates the temperature inhomogeneity increase, while the increasing salinity inhomogeneity is mainly due to the amplified salinity contrast between the subtropical and subpolar latitudes.