Atmosphere teleconnections from abatement of China aerosol emissions exacerbate Northeast Pacific warm blob events

During 2010 to 2020, Northeast Pacific (NEP) sea surface temperature (SST) experienced the warmest decade ever recorded, manifested in several extreme marine heatwaves, referred to as "warm blob" events, which severely affect marine ecosystems and extreme weather along the west coast of North America. While year-to-year internal climate variability has been suggested as a cause of individual events, the causes of the continuous dramatic NEP SST warming remain elusive. Here, we show that other than the greenhouse gas (GHG) forcing, rapid aerosol abatement in China over the period likely plays an important role. Anomalous tropospheric warming induced by declining aerosols in China generated atmospheric teleconnections from East Asia to the NEP, featuring an intensified and southward-shifted Aleutian Low. The associated atmospheric circulation anomaly weakens the climatological westerlies in the NEP and warms the SST there by suppressing the evaporative cooling. The aerosol-induced mean warming of the NEP SST, along with internal climate variability and the GHG-induced warming, made the warm blob events more frequent and intense during 2010 to 2020. As anthropogenic aerosol emissions continue to decrease, there is likely to be an increase in NEP warm blob events, disproportionately large beyond the direct radiative effects.

Aerosol demasking enhances climate warming over South Asia

Anthropogenic aerosols mask the climate warming caused by greenhouse gases (GHGs). In the absence of observational constraints, large uncertainties plague the estimates of this masking effect. Here we used the abrupt reduction in anthropogenic emissions observed during the COVID-19 societal slow-down to characterize the aerosol masking effect over South Asia. During this period, the aerosol loading decreased substantially and our observations reveal that the magnitude of this aerosol demasking corresponds to nearly three-fourths of the CO2-induced radiative forcing over South Asia. Concurrent measurements over the northern Indian Ocean unveiled a ~7% increase in the earth's surface-reaching solar radiation (surface brightening). Aerosol-induced atmospheric solar heating decreased by ~0.4 K d-1. Our results reveal that under clear sky conditions, anthropogenic emissions over South Asia lead to nearly 1.4 W m-2 heating at the top of the atmosphere during the period March-May. A complete phase-out of today's fossil fuel combustion to zero-emission renewables would result in rapid aerosol demasking, while the GHGs linger on.

Optimization of N-PERT Solar Cell under Atacama Desert Solar Spectrum

In the Atacama Desert, the spectral distribution of solar radiation differs from the global standard, showing very high levels of irradiation with a particularly high ultraviolet content. Additionally, the response of photovoltaic (PV) technologies is spectrally dependent, so it is necessary to consider local conditions and type of technology to optimize PV devices since solar cells are usually designed for maximum performance under standard testing conditions (STC). In this work, we determined geometrical and doping parameters to optimize the power of an n-type bifacial passivated emitter and rear totally diffused solar cell (n-PERT). Six parameters (the thicknesses of cell, emitter, and back surface field, as well as doping concentration of emitter, base, and back surface field) were used to optimize the cell under the Atacama Desert spectrum (AM 1.08) and under standard conditions (AM 1.5) through a genetic algorithm. To validate the model, the calculated performance of the n-PERT cell was compared with experimental measurements. Computed and experimental efficiencies showed a relative difference below 1% under STC conditions. Through the optimization process, we found that different geometry and doping concentrations are necessary for cells to be used in the Atacama Desert. Reducing the thickness of all layers and increasing doping can lead to a relative increment of 5.4% in the cell efficiency under AM 1.08. Finally, we show the potential effect of metallization and the viability of reducing the thicknesses of the emitter and the back surface field.

On the Exploitation of Remote Sensing Technologies for the Monitoring of Coastal and River Delta Regions

Remote sensing technologies are extensively applied to prevent, monitor, and forecast hazardous risk conditions in the present-day global climate change era. This paper presents an overview of the current stage of remote sensing approaches employed to study coastal and delta river regions. The advantages and limitations of Earth Observation technology in characterizing the effects of climate variations on coastal environments are also presented. The role of the constellations of satellite sensors for Earth Observation, collecting helpful information on the Earth’s system and its temporal changes, is emphasized. For some key technologies, the principal characteristics of the processing chains adopted to obtain from the collected raw data added-value products are summarized. Emphasis is put on studying various disaster risks that affect coastal and megacity areas, where heterogeneous and interlinked hazard conditions can severely affect the population.

Machine-Learning Based Analysis of Liquid Water Path Adjustments to Aerosol Perturbations in Marine Boundary Layer Clouds Using Satellite Observations

Changes in marine boundary layer cloud (MBLC) radiative properties in response to aerosol perturbations are largely responsible for uncertainties in future climate predictions. In particular, the relationship between the cloud droplet number concentration (Nd, a proxy for aerosol) and the cloud liquid water path (LWP) remains challenging to quantify from observations. In this study, satellite observations from multiple polar-orbiting platforms for 2006–2011 are used in combination with atmospheric reanalysis data in a regional machine learning model to predict changes in LWP in MBLCs in the Southeast Atlantic. The impact of predictor variables on the model output is analysed using Shapley values as a technique of explainable machine learning. Within the machine learning model, precipitation fraction, cloud top height, and Nd are identified as important cloud state predictors for LWP, with dynamical proxies and sea surface temperature (SST) being the most important environmental predictors. A positive nonlinear relationship between LWP and Nd is found, with a weaker sensitivity at high cloud droplet concentrations. This relationship is found to be dependent on other predictors in the model: Nd–LWP sensitivity is higher in precipitating clouds and decreases with increasing SSTs.

Theoretical Uncertainty Analysis of Satellite Retrieved Aerosol Optical Depth Associated with Surface Albedo and Aerosol Optical Properties

Deriving aerosol optical depth (AOD) from space-borne observations is still challenging due to uncertainties associated with sensor calibration drift, cloud screening, aerosol type classification, and surface reflectance characterization. As an initial step to understanding the physical processes impacting these uncertainties in satellite AOD retrievals, this study outlines a theoretical approach to estimate biases in the satellite aerosol retrieval algorithm affected by surface albedo and prescribed aerosol optical properties using a simplified radiative transfer model with a traditional error propagation approach. We expand the critical surface reflectance concept to obtain the critical surface albedo (CSA), critical single scattering albedo (CSSA), and critical asymmetry parameter (CAP). The top-of-atmosphere (TOA) reflectance is not sensitive to significant variability in aerosol loading (AOD) at the critical value; thus, the AOD cannot be determined. Results show that 5% bias in surface albedo (A), single scattering albedo (SSA), or asymmetry parameter (g) lead to large retrieved AOD errors, especially high under conditions when A, SSA, or g are close to their critical values. The results can be useful for future research related to improvements of satellite aerosol retrieval algorithms and provide a preliminary framework to analytically quantify AOD uncertainties from satellite retrievals.

Estimation of Surface Downward Shortwave Radiation over China from Himawari-8 AHI Data Based on Random Forest

Downward shortwave radiation (RS) drives many processes related to atmosphere–surface interactions and has great influence on the earth’s climate system. However, ground-measured RS is still insufficient to represent the land surface, so it is still critical to generate high accuracy and spatially continuous RS data. This study tries to apply the random forest (RF) method to estimate the RS from the Himawari-8 Advanced Himawari Imager (AHI) data from February to May 2016 with a two-km spatial resolution and a one-day temporal resolution. The ground-measured RS at 86 stations of the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA) are collected to evaluate the estimated RS data from the RF method. The evaluation results indicate that the RF method is capable of estimating the RS well at both the daily and monthly time scales. For the daily time scale, the evaluation results based on validation data show an overall R value of 0.92, a root mean square error (RMSE) value of 35.38 (18.40%) Wm−2, and a mean bias error (MBE) value of 0.01 (0.01%) Wm−2. For the estimated monthly RS, the overall R was 0.99, the RMSE was 7.74 (4.09%) Wm−2, and the MBE was 0.03 (0.02%) Wm−2 at the selected stations. The comparison between the estimated RS data over China and the Clouds and Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) RS dataset was also conducted in this study. The comparison results indicate that the RS estimates from the RF method have comparable accuracy with the CERES-EBAF RS data over China but provide higher spatial and temporal resolution.

Seasonal Variations of Arctic Low-Level Clouds and Its Linkage to Sea Ice Seasonal Variations

Using CALIPSO-CloudSat-Clouds and the Earth's Radiant Energy System-Moderate Resolution Imaging Spectrometer data set, this study documents the seasonal variation of sea ice, cloud, and atmospheric properties in the Arctic (70°N-82°N) for 2007-2010. A surface-type stratification-consisting Permanent Ocean, Land, Permanent Ice, and Transient Sea Ice-is used to investigate the influence of surface type on low-level Arctic cloud liquid water path (LWP) seasonality. The results show significant variations in the Arctic low-level cloud LWP by surface type linked to differences in thermodynamic state. Subdividing the Transient Ice region (seasonal sea ice zone) by melt/freeze season onset dates reveals a complex influence of sea ice variations on low cloud LWP seasonality. We find that lower tropospheric stability is the primary factor affecting the seasonality of cloud LWP. Our results suggest that variations in sea ice melt/freeze onset have a significant influence on the seasonality of low-level cloud LWP by modulating the lower tropospheric thermal structure and not by modifying the surface evaporation rate in late spring and midsummer. We find no significant dependence of the May low-level cloud LWP peak on the melt/freeze onset dates, whereas and September/October low-level cloud LWP maximum shifts later in the season for earlier melt/later freeze onset regions. The Arctic low cloud LWP seasonality is controlled by several surface-atmosphere interaction processes; the importance of each varies seasonally due to the thermodynamic properties of sea ice. Our results demonstrate that when analyzing Arctic cloud-sea ice interactions, a seasonal perspective is critical.

High cloud coverage over melted areas dominates the impact of clouds on the albedo feedback in the Arctic

Warming in the Arctic is larger than the global average. A primary reason for this Arctic Amplification is the albedo feedback. The contrasting albedo of sea ice and dark melted surface areas is the key component of albedo feedback. Cloud coverage over the changing surface and the response of the clouds to the changing surface conditions will modify the change in planetary albedo when sea ice melts. Space-based lidar measurements provide a unique opportunity for cloud measurements in the Arctic. The response of clouds to the changing sea ice concentration was directly observed. Based on CALIPSO satellite observations of cloud properties, this study found that cloud coverage in ice-free regions in the Arctic linearly increased with the area of ice-free water during the melt seasons in the past 10 years, while sea ice coverage varies significantly year-to-year. The observations suggest that when sea-ice retreats, cloud fraction of the ice-free region remains fixed at nearly 81%. The high cloud coverage over melted areas significantly reduces the albedo feedback. These results indicate that space-based lidar cloud and surface observations of the Arctic can help constrain and improve climate models.

Observation-Based Decomposition of Radiative Perturbations and Radiative Kernels

The Clouds and the Earth's Radiant Energy System (CERES)-partial radiative perturbation [PRP (CERES-PRP)] methodology applies partial-radiative-perturbation-like calculations to observational datasets to directly isolate the individual cloud, atmospheric, and surface property contributions to the variability of the radiation budget. The results of these calculations can further be used to construct radiative kernels. A suite of monthly mean observation-based inputs are used for the radiative transfer, including cloud properties from either the diurnally resolved passive-sensor-based CERES synoptic (SYN) data or the combination of the CloudSat cloud radar and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar. The CloudSat/CALIPSO cloud profiles are incorporated via a clustering method that obtains monthly mean cloud properties suitable for accurate radiative transfer calculations. The computed fluxes are validated using the TOA fluxes observed by CERES. Applications of the CERES-PRP methodology are demonstrated by computing the individual contributions to the variability of the radiation budget over multiple years and by deriving water vapor radiative kernels. The calculations for the former are used to show that an approximately linear decomposition of the total flux anomalies is achieved. The observation-based water vapor kernels were used to investigate the accuracy of the GCM-based NCAR CAM3.0 water vapor kernel. Differences between our observation-based kernel and the NCAR one are marginally larger than those inferred by previous comparisons among different GCM kernels.

Space lidar observations constrain longwave cloud feedback

Some of the most challenging questions in atmospheric science relate to how clouds will respond as the climate warms. On centennial scales, the response of clouds could either weaken or enhance the warming due to greenhouse gas emissions. Here we use space lidar observations to quantify changes in cloud altitude, cover, and opacity over the oceans between 2008 and 2014, together with a climate model with a lidar simulator to also simulate these changes in the present-day climate and in a future, warmer climate. We find that the longwave cloud altitude feedback, found to be robustly positive in simulations since the early climate models and backed up by physical explanations, is not the dominant longwave feedback term in the observations, although it is in the model we have used. These results suggest that the enhanced longwave warming due to clouds might be overestimated in climate models. These results highlight the importance of developing a long-term active sensor satellite record to reduce uncertainties in cloud feedbacks and prediction of future climate.

Effects of soiling on photovoltaic (PV) modules in the Atacama Desert

Soiling by dry deposition affects the power output of photovoltaic (PV) modules, especially under dry and arid conditions that favor natural atmospheric aerosols (wind-blown dust). In this paper, we report on measurements of the soiling effect on the energy yield of grid-connected crystalline silicon PV modules deployed in five cities across a north-south transect of approximately 1300 km in the Atacama Desert ranging from latitude 18°S to latitude 30°S. Energy losses were assessed by comparing side-by-side outputs of four co-planar PV modules. Two of the PV modules of the array were kept clean as a control, while we allowed the other two to naturally accumulate soiling for 12 months (from January 2017 to January 2018). We found that the combination of high deposition rates and infrequent rainfalls led to annual energy losses that peaked at 39% in the northern coastal part of the desert. In contrast, annual energy losses of 3% or less were measured at relatively high-altitude sites and also at locations in the southern part of the desert. For comparison, soiling-induced annual energy losses of about 7% were measured in Santiago, Chile (33°S), a major city with higher rainfall frequency but where urban pollution plays a significant role.

Ultraviolet radiation in the Atacama Desert

The world's highest levels of surface ultraviolet (UV) irradiance have been measured in the Atacama Desert. This area is characterized by its high altitude, prevalent cloudless conditions, and a relatively low total ozone column. In this paper, we provide estimates of the surface UV (monthly UV index at noon and annual doses of UV-B and UV-A) for all sky conditions in the Atacama Desert. We found that the UV index at noon during the austral summer is expected to be greater than 11 in the whole desert. The annual UV-B (UV-A) doses were found to range from about 3.5 kWh/m² (130 kWh/m²) in coastal areas to 5 kWh/m² (160 kWh/m²) on the Andean plateau. Our results confirm significant interhemispherical differences. Typical annual UV-B doses in the Atacama Desert are about 40% greater than typical annual UV-B doses in northern Africa. Mostly due to seasonal changes in the ozone, the differences between the Atacama Desert and northern Africa are expected to be about 60% in the case of peak UV-B levels (i.e. the UV-B irradiances at noon close to the summer solstice in each hemisphere). Interhemispherical differences in the UV-A are significantly lower since the effect of the ozone in this part of the spectrum is minor.

Observational Constraints on Cloud Feedbacks: The Role of Active Satellite Sensors

Cloud profiling from active lidar and radar in the A-train satellite constellation has significantly advanced our understanding of clouds and their role in the climate system. Nevertheless, the response of clouds to a warming climate remains one of the largest uncertainties in predicting climate change and for the development of adaptions to change. Both observation of long-term changes and observational constraints on the processes responsible for those changes are necessary. We review recent progress in our understanding of the cloud feedback problem. Capabilities and advantages of active sensors for observing clouds are discussed, along with the importance of active sensors for deriving constraints on cloud feedbacks as an essential component of a global climate observing system.

Cloud Occurrences and Cloud Radiative Effects (CREs) from CCCM and CloudSat Radar-Lidar Products

Two kinds of radar-lidar synergy cloud products are compared and analyzed in this study; CERES-CALIPSO-CloudSat-MODIS (CCCM) product and CloudSat radar-lidar (RL) product such as GEOPROF-LIDAR and FLXHR-LIDAR. Compared to GEOPROF-LIDAR, CCCM has more low-level (< 1 km) clouds over tropical oceans because CCCM uses a more relaxed threshold of Cloud-Aerosol Discrimination (CAD) score for Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical feature mask (VFM) product. In contrast, GEOPROF-LIDAR has more mid-level (1-8 km) clouds than CCCM at high latitudes (> 40°). The difference occurs when hydrometeors are detected by CALIPSO lidar but are undetected by CloudSat radar, which may be related to precipitation. In the comparison of cloud radiative effects (CREs), global mean differences between CCCM and FLXHR-LIDAR are mostly smaller than 5 W m-2, while noticeable regional differences are found over three regions. First, CCCM has larger shortwave (SW) and longwave (LW) CREs than FXLHR-LIDAR along the west coasts of Africa and America. This might be caused by missing small-scale marine boundary layer clouds in FLXHR-LIDAR. Second, over tropical oceans where precipitation frequently occurs, SW and LW CREs from FLXHR-LIDAR are larger than those from CCCM partly because FLXHR-LIDAR algorithm includes the contribution of rainwater to total liquid water path. Third, over midlatitude storm-track regions, CCCM shows larger SW and LW CREs than FLXHR-LIDAR, due to CCCM biases caused by larger cloud optical depth or higher cloud effective height.

Enhanced wintertime greenhouse effect reinforcing Arctic amplification and initial sea-ice melting

The speeds of both Arctic surface warming and sea-ice shrinking have accelerated over recent decades. However, the causes of this unprecedented phenomenon remain unclear and are subjects of considerable debate. In this study, we report strong observational evidence, for the first time from long-term (1984-2014) spatially complete satellite records, that increased cloudiness and atmospheric water vapor in winter and spring have caused an extraordinary downward longwave radiative flux to the ice surface, which may then amplify the Arctic wintertime ice-surface warming. In addition, we also provide observed evidence that it is quite likely the enhancement of the wintertime greenhouse effect caused by water vapor and cloudiness has advanced the time of onset of ice melting in mid-May through inhibiting sea-ice refreezing in the winter and accelerating the pre-melting process in the spring, and in turn triggered the positive sea-ice albedo feedback process and accelerated the sea ice melting in the summer.

Estimation of daily photosynthetically active radiation (PAR) in presence of low to high aerosol loads: application to OLCI-like satellite data

Estimation of daily photosynthetically active radiation (PAR) is of primary importance for monitoring the ocean primary production and the subsequent production of carbon by phytoplankton at global scale from remote sensing ocean color sensors. On the other hand, aerosol abundance and composition play a critical role in the modulation of PAR. In this study, an original algorithm, so-called OLCIPAR, is proposed for routinely determining the daily PAR from optical satellite sensors such as the OLCI sensor aboard Sentinel-3 (ESA). The OLCIPAR algorithm has been developed to overcome some of the limitations of the current existing methods. In particular, multiple scattering effects induced by the atmospheric layer are taken into account based on exact radiative transfer calculations. Another advantage of OLCIPAR method is to consider a great variety of aerosol models to better account for their optical variability as observed in real world conditions. The OLCIPAR algorithm was applied to the archive of MERIS data, whose sensor is similar to OLCI. The validation of the retrieved daily PAR was carried out based on comparison with the time series acquired by the BOUSSOLE oceanographic buoy moored in the Mediterranean Sea. Results show a regression slope of 1% and an accuracy within 10% which confirms the robustness of the algorithm. The comparison of OLCIPAR retrievals with the products routinely distributed by NASA shows that estimates of PAR differ by up to 20% in the subtropical Atlantic Ocean where important amounts of dust aerosols are present. The improvements brought by OLCIPAR method for deriving the daily PAR could thus permit to better assess the impact of aerosols on reduction of PAR with implications on the estimation of oceanic primary production.

Overview of MPLNET Version 3 Cloud Detection

The National Aeronautics and Space Administration Micropulse Lidar Network Version 3 cloud detection algorithm is described and its differences relative to the previous version highlighted. Clouds are identified from normalized Level 1 signal profiles using two complementary methods. The first considers signal derivatives vertically for resolving low-level clouds. The second, which resolves high-level clouds like cirrus, is based on signal uncertainties given the relatively low signal-to-noise ratio exhibited in the upper troposphere by eye-safe network instruments, especially during daytime. Furthermore, a multi-temporal averaging scheme is used to improve cloud detection under conditions of weak signal-to-noise. Diurnal and seasonal cycles of cloud occurrence frequency based on one year of measurements at the Goddard Space Flight Center (Greenbelt, MD) site are compared for the new and previous versions. The largest differences, and perceived improvement, in detection occurs for high clouds (above 5-km, mean sea level) which increase in occurrence by nearly 6%. There is also an increase in the detection of multi-layered cloud profiles from 9% to 20%. Macrophysical properties and estimates of cloud optical depth are presented for a transparent cirrus dataset. However, the limit to which molecular signal can be reliably retrieved above cirrus clouds occurs between cloud optical depths of 0.5 and 0.8.

The Solar Spectrum in the Atacama Desert

The Atacama Desert has been pointed out as one of the places on earth where the highest surface irradiance may occur. This area is characterized by its high altitude, prevalent cloudless conditions and relatively low columns of ozone and water vapor. Aimed at the characterization of the solar spectrum in the Atacama Desert, we carried out in February-March 2015 ground-based measurements of the spectral irradiance (from the ultraviolet to the near infrared) at seven locations that ranged from the city of Antofagasta (on the southern pacific coastline) to the Chajnantor Plateau (5,100 m altitude). Our spectral measurements allowed us to retrieve the total ozone column, the precipitable water, and the aerosol properties at each location. We found that changes in these parameters, as well as the shorter optical path length at high-altitude locations, lead to significant increases in the surface irradiance with the altitude. Our measurements show that, in the range 0–5100 m altitude, surface irradiance increases with the altitude by about 27% in the infrared range, 6% in the visible range, and 20% in the ultraviolet range. Spectral measurements carried out at the Izaña Observatory (Tenerife, Spain), in Hannover (Germany) and in Santiago (Chile), were used for further comparisons.

An observational radiative constraint on hydrologic cycle intensification

Intensification of the hydrologic cycle is a key dimension of climate change, with substantial impacts on human and natural systems. A basic measure of hydrologic cycle intensification is the increase in global-mean precipitation per unit surface warming, which varies by a factor of three in current-generation climate models (about 1-3 per cent per kelvin). Part of the uncertainty may originate from atmosphere-radiation interactions. As the climate warms, increases in shortwave absorption from atmospheric moistening will suppress the precipitation increase. This occurs through a reduction of the latent heating increase required to maintain a balanced atmospheric energy budget. Using an ensemble of climate models, here we show that such models tend to underestimate the sensitivity of solar absorption to variations in atmospheric water vapour, leading to an underestimation in the shortwave absorption increase and an overestimation in the precipitation increase. This sensitivity also varies considerably among models due to differences in radiative transfer parameterizations, explaining a substantial portion of model spread in the precipitation response. Consequently, attaining accurate shortwave absorption responses through improvements to the radiative transfer schemes could reduce the spread in the predicted global precipitation increase per degree warming for the end of the twenty-first century by about 35 per cent, and reduce the estimated ensemble-mean increase in this quantity by almost 40 per cent.

Covariance between Arctic sea ice and clouds within atmospheric state regimes at the satellite footprint level

Understanding the cloud response to sea ice change is necessary for modeling Arctic climate. Previous work has primarily addressed this problem from the interannual variability perspective. This paper provides a refined perspective of sea ice-cloud relationship in the Arctic using a satellite footprint-level quantification of the covariance between sea ice and Arctic low cloud properties from NASA A-Train active remote sensing data. The covariances between Arctic low cloud properties and sea ice concentration are quantified by first partitioning each footprint into four atmospheric regimes defined using thresholds of lower tropospheric stability and midtropospheric vertical velocity. Significant regional variability in the cloud properties is found within the atmospheric regimes indicating that the regimes do not completely account for the influence of meteorology. Regional anomalies are used to account for the remaining meteorological influence on clouds. After accounting for meteorological regime and regional influences, a statistically significant but weak covariance between cloud properties and sea ice is found in each season for at least one atmospheric regime. Smaller average cloud fraction and liquid water are found within footprints with more sea ice. The largest-magnitude cloud-sea ice covariance occurs between 500 m and 1.2 km when the lower tropospheric stability is between 16 and 24 K. The covariance between low cloud properties and sea ice is found to be largest in fall and is accompanied by significant changes in boundary layer temperature structure where larger average near-surface static stability is found at larger sea ice concentrations.

Reconciling spatial and temporal soil moisture effects on afternoon rainfall

Soil moisture impacts on precipitation have been strongly debated. Recent observational evidence of afternoon rain falling preferentially over land parcels that are drier than the surrounding areas (negative spatial effect), contrasts with previous reports of a predominant positive temporal effect. However, whether spatial effects relating to soil moisture heterogeneity translate into similar temporal effects remains unknown. Here we show that afternoon precipitation events tend to occur during wet and heterogeneous soil moisture conditions, while being located over comparatively drier patches. Using remote-sensing data and a common analysis framework, spatial and temporal correlations with opposite signs are shown to coexist within the same region and data set. Positive temporal coupling might enhance precipitation persistence, while negative spatial coupling tends to regionally homogenize land surface conditions. Although the apparent positive temporal coupling does not necessarily imply a causal relationship, these results reconcile the notions of moisture recycling with local, spatially negative feedbacks.

The sign of soil moisture–precipitation feedback has been strongly debated. Here, the authors show that rain tends to fall where soils are drier than their surroundings, but on days with overall wet and heterogeneous conditions, explaining the apparent contradictions between recent studies.