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Abstract. ​​​​​​​Land–atmosphere coupling (LAC) has long been studied, focusing on land surface and atmospheric boundary layer processes. However, the influence of humidity in the lower troposphere (LT), especially that above the planetary boundary layer (PBL), on LAC remains largely unexplored. In this study, we use radiosonde observations from the US Southern Great Plains (SGP) site and an entrained parcel buoyancy model to investigate the impact of LT humidity on LAC there during the warm season (May–September). We quantify the effect of LT humidity on convective buoyancy by measuring the difference between the 2–4 km vertically integrated buoyancy with the influence of background LT humidity and that without it. Our results show that, under dry soil conditions, anomalously high LT humidity is necessary to produce the buoyancy profiles required for afternoon precipitation events (APEs). These APEs under dry soil moisture cannot be explained by commonly used local LAC indices such as the convective triggering potential and low-level humidity index (CTP / HILow), which do not account for the influence of the LT humidity. On the other hand, consideration of LT humidity is unnecessary to explain APEs under wet soil moisture conditions, suggesting that the boundary layer moisture alone could be sufficient to generate the required buoyancy profiles. These findings highlight the need to consider the impact of LT humidity, which is often decoupled from the humidity near the surface and is largely controlled by moisture transport, in understanding land–atmospheric feedbacks under dry soil conditions, especially during droughts or dry spells over the SGP.

 

Numerical experiments on sensitivity to land surface initializations are frequently conducted to investigate the predictability and uncertainties of hydrometeorological extremes. However, the conventional approaches to soil initialization often assume synchronized extremes over the target region, creating initial conditions that violate the intrinsic spatial pattern of hydrometeorological variability. Here we propose a “Slope” approach to accommodate unsynchronized anomalies, which creates initial conditions of a variable (soil temperature or soil moisture) across a large domain based on the slopes of linear regression between the variable averaged over a small target region and at each grid point in the surrounding regions. Within the target region, the “Slope” approach produces spatial patterns and temporal evolutions of hydrometeorological responses similar to the conventional approach, but generates stronger signals probably due to the nonlocal impact (excluded from the conventional approach). In the surrounding regions, the hydrometeorological responses in the “Slope” approach are consistent with the spatiotemporal variability of the model climate. Slope‐based experiments targeting different adjacent regions produce similar results, suggesting that one ensemble of experiments targeting one region may be sufficient to represent the responses from multiple ensembles each targeting a different region and thus providing the basis for increasing the computational efficiency of some land‐atmosphere interaction studies. While South America is used to demonstrate the concept in this study, the new approach offers the most advantages in regions with spatially unsynchronized or even anti‐phased hydrometeorological extremes.

 

Flash drought often leads to devastating effects in multiple sectors and presents a unique challenge for drought early warning due to its sudden onset and rapid intensification. Existing drought monitoring and early warning systems are based on various hydrometeorological variables reaching thresholds of unusually low water content. Here, we propose a flash drought early warning approach based on spaceborne measurements of solar-induced chlorophyll fluorescence (SIF), a proxy of photosynthesis that captures plant response to multiple environmental stressors. Instead of negative SIF anomalies, we focus on the subseasonal trajectory of SIF and consider slower-than-usual increase or faster-than-usual decrease of SIF as an early warning for flash drought onset. To quantify the deviation of SIF trajectory from the climatological norm, we adopt existing formulas for a rapid change index (RCI) and apply the RCI analysis to spatially downscaled 8-d SIF data from GOME-2 during 2007–2018. Using two well-known flash drought events identified by the operational US Drought Monitor (in 2012 and 2017), we show that SIF RCI can produce strong predictive signals of flash drought onset with a lead time of 2 wk to 2 mo and can also predict drought recovery with several weeks of lead time. While SIF RCI shows great early warning potential, its magnitude diminishes after drought onset and therefore cannot reflect the current drought intensity. With its long lead time and direct relevance for agriculture, SIF RCI can support a global early warning system for flash drought and is especially useful over regions with sparse hydrometeorological data. 

Abstract Although the intensity of extreme precipitation is predicted to increase with climate warming, at the weather scale precipitation extremes over most of the globe decrease when temperature exceeds a certain threshold, and the spatial extent of this negative scaling is projected to increase as the climate warms. The nature and cause of the negative scaling at high temperature and its implications remain poorly understood. Based on sub-daily data from observations, reanalysis data, and output from a coarse-resolution (∼200 km) global model and a fine-resolution (4 km) convection-permitting regional model, we show that the negative scaling is primarily a reflection of high temperature suppressing precipitation over land and storm-induced temperature variation over the ocean. We further identify the high temperature-induced increase of saturation deficit as a critical condition for the negative scaling of extreme precipitation over land. Large saturation deficit reduces precipitation intensity by slowing down the convective updraft condensation rate and accelerating condensate evaporation. The heat-induced suppression of precipitation, both for its mean and extremes, provides one mechanism for the co-occurrence of drought and heatwaves. As the saturation deficit over land is expected to increase in a warmer climate, our results imply a growing prevalence of negative scaling, potentially increasing the frequency of compound drought and heat events. Understanding the physical mechanisms underlying the negative scaling of precipitation at high temperature is, therefore, essential for assessing future risks of extreme events, including not only flood due to extreme precipitation but also drought and heatwaves. 

Abstract This study assesses the ecohydrological effects of recent meteorological droughts in tropical South America based on multiple sources of data, and investigates the possible mechanisms underlying the drought response and recovery of different ecohydrological systems. Soil drought response and recovery lag behind the meteorological drought, with delays longer in the dry region (Nordeste) than in the wet region (Amazonia), and longer in deep soil than in shallow soil. Evapotranspiration (ET) and vegetation in Nordeste are limited by water under normal conditions and decrease promptly in response to the onset of shallow soil drought. In most of the Amazon where water is normally abundant, ET and vegetation indices follow an increase-then-decrease pattern, increase at the drought onset due to increased sunshine and decrease when the drought is severe enough to cause a shift from an energy-limited regime to a water-limited regime. After the demise of meteorological droughts, ET and vegetation rapidly recover in Nordeste with the replenishment of shallow soil moisture (SM), but take longer to recover in southern Amazon due to their dependence on deep SM storage. Following severe droughts, the negative anomalies of ET and vegetation indices in southern Amazon tend to persist well beyond the end of soil drought, indicating drought-induced forest mortality that is slow to recover from. Findings from this study may have implications on the possibility of a future forest dieback as drought is projected to become more frequent and more severe in a warmer climate. 

In comparison with conventional content delivery networks, peer-to-peer (p2p) content delivery is promising to save cost and handle high peak-demand, and can also complement the decentralized storage networks such as Filecoin. However, reliable p2p delivery requires proper enforcement of delivery fairness, i.e., the deliverers should be rewarded according to their in-time delivery. Unfortunately, most existing studies on delivery fairness are based on non-cooperative game-theoretic assumptions that are arguably unrealistic in the ad-hoc p2p setting. We for the first time put forth an expressive yet still minimalist security notion for desired fair p2p content delivery, and give two efficient solutions 𝖥𝖺𝗂𝗋𝖣𝗈𝗐𝗇𝗅𝗈𝖺𝖽 and 𝖥𝖺𝗂𝗋𝖲𝗍𝗋𝖾𝖺𝗆 via the blockchain for p2p downloading and p2p streaming scenarios, respectively. Our designs not only guarantee delivery fairness to ensure deliverers be paid (nearly) proportional to their in-time delivery but also ensure the content consumers and content providers are fairly treated. The fairness of each party can be guaranteed when the other two parties collude to arbitrarily misbehave. Moreover, the systems are efficient in the sense of attaining nearly asymptotically optimal on-chain costs and deliverer communication. We implement the protocols and build the prototype systems atop the Ethereum Ropsten network. Extensive experiments done in LAN and WAN settings showcase their high practicality. 

Abstract This study investigates the potential effects of historical deforestation in South America using a regional climate model driven with reanalysis data. Two different sources of data were used to quantify deforestation during the 1980s to 2010s, leading to two scenarios of forest loss: smaller but spatially continuous in scenario 1 and larger but spatially scattered in scenario 2. The model simulates a generally warmer and drier local climate following deforestation. Vegetation canopy becomes warmer due to reduced canopy evapotranspiration, and ground becomes warmer due to more radiation reaching the ground. The warming signal for surface air is weaker than for ground and vegetation, likely due to reduced surface roughness suppressing the sensible heat flux. For surface air over deforested areas, the warming signal is stronger for the nighttime minimum temperature and weaker or even becomes a cooling signal for the daytime maximum temperature, due to the strong radiative effects of albedo at midday, which reduces the diurnal amplitude of temperature. The drying signals over deforested areas include lower atmospheric humidity, less precipitation, and drier soil. The model identifies the La Plata basin as a region remotely influenced by deforestation, where a simulated increase of precipitation leads to wetter soil, higher ET, and a strong surface cooling. Over both deforested and remote areas, the deforestation-induced surface climate changes are much stronger in scenario 2 than scenario 1; coarse-resolution data and models (such as in scenario 1) cannot represent the detailed spatial structure of deforestation and underestimate its impact on local and regional climates. 

For countries dependent on rainfed agriculture, failure of the rainy season can lead to serious consequences on the broader economy. Maize, a common staple crop in these countries, often expresses significant interannual variability, given its high sensitivity to water stress. It is traditionally planted at rainy season onset to maximize the growing season and potential yield; however, this risks planting during a ‘false onset’ that can damage the crop or require replanting. Rainy season onset forecasts offer some promise in reducing this risk; however, the potential for increasing yield has not been explicitly quantified. This study quantifies the yield gap associated with suboptimal maize planting times using a process-based crop model over a 36 year historical period across Ethiopia. Onset-informed and forecast-informed approaches are compared with a baseline approach, and results indicate a strong potential for yield gains in drier regions as well as reductions in interannual variance countrywide. In contrast, regions with reliably sufficient precipitation illustrate only minimal gains. In general, integration of onset forecasts into agricultural decision-making warrants inclusion in agricultural extension efforts.