Identifying environmental drivers of greenhouse gas emissions under warming and reduced rainfall in boreal-temperate forests
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A theory is developed which establishes the basis for the use of rainfall areas within present thresholds as a measure of either the instantaneous areawide rain rate of convective storms or the total volume of rain from an individual storm over its lifetime. The method is based upon the existence of a well-behaved pdf of rain rate either from the many storms at one instant or from a single storm during its life. The generality of the instantaneous areawide method was examined by applying it to quantitative radar data sets from the GARP Tropical Atlantic Experiment for South Africa, Texas, and Darwin (Australia). It is shown that the pdf's developed for each of these areas are consistent with the theory.
Tropical forests contain some of the largest soil carbon (C) stocks on Earth, making them broadly relevant to terrestrial-climate feedbacks, yet our understanding of how their soil organic C (SOC) fractions vary over space and time is limited. We studied effects of season, fertility, and mean annual precipitation (MAP) on the C contents of soil fractions across 14 lowland forests in Panama. We measured free-debris, occluded-debris, and mineral-associated SOC fractions, as well as soluble C associated with each fraction. We hypothesized that mineral-associated SOC would be greatest in infertile, strongly weathered soils with large reactive mineral contents. We also hypothesized that the debris SOC fractions would accumulate during the dry season, reflecting seasonal increases in litterfall. To address this, we compared soil fractions in wet and dry seasons from fertile and infertile soils across a range of 1809 – 2864 mm MAP. The C content (mg C / g soil) of all soil fractions varied with fertility and MAP: specifically, free-debris SOC was greatest in wet, high-fertility soils, and occluded-debris SOC was greater in high-fertility than low-fertility soils. The mineral-associated SOC fraction, which contained the majority of bulk soil C, showed increasing C content with greater MAP in infertile sites, presumably driving similar spatial patterns in the bulk soil. Only the free-debris SOC fraction showed strong seasonal variation, increasing in mass during the dry season. Nitrogen behaved similarly to C. In summary, soil C contents increased with MAP in infertile sites but not fertile sites, driven by the mineral-associated SOC fraction. The dry season had greater free-debris SOC, but this seasonal trend was not apparent in bulk soil C, likely because of the small size of the free-debris SOC fraction. Furthermore, changes in the quantity and seasonality of precipitation, which are projected for tropical forests, might shift spatial and temporal patterns of soil C storage, which would in turn influence forest-climate feedbacks for this C-rich biome.
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The nucleation of atmospheric vapours is an important source of new aerosol particles that can subsequently grow to form cloud condensation nuclei in the atmosphere. Most field studies of atmospheric aerosols over continents are influenced by atmospheric vapours of anthropogenic origin and, in consequence, aerosol processes in pristine, terrestrial environments remain poorly understood. The Amazon rainforest is one of the few continental regions where aerosol particles and their precursors can be studied under near-natural conditions, but the origin of small aerosol particles that grow into cloud condensation nuclei in the Amazon boundary layer remains unclear. Here we present aircraft- and ground-based measurements under clean conditions during the wet season in the central Amazon basin. We find that high concentrations of small aerosol particles (with diameters of less than 50 nanometres) in the lower free troposphere are transported from the free troposphere into the boundary layer during precipitation events by strong convective downdrafts and weaker downward motions in the trailing stratiform region. Lastly, this rapid vertical transport can help to maintain the population of particles in the pristine Amazon boundary layer, and may therefore influence cloud properties and climate under natural conditions.
Water is the single most important element of life. Rainfall plays an important role in the spatial and temporal distribution of this precious natural resource, and it has a direct impact on agricultural production, daily life activities, and human health. One of the important elements that govern rainfall formation and distribution is atmospheric aerosol, which also affects the Earth's radiation balance and climate. Therefore, understanding how dust compositions and distributions affect the regional rainfall pattern is crucial, particularly in regions with high atmospheric dust loads such as the Middle East. Although aerosol and rainfall research has garnered increasing attention as both an independent and interdisciplinary topic in the last few decades, the details of various direct and indirect pathways by which dust affects rainfall are not yet fully understood. Here, we explored the effects of dust on rainfall formation and distribution as well as the physical mechanisms that govern these phenomena, using high-resolution WRF-Chem simulations (~1.5 km × 1.5 km) configured with an advanced double-moment cloud microphysics scheme coupled with a sectional eight-bin aerosol scheme. Our model-simulated results were realistic, as evaluated from multiple perspectives including vertical profiles of aerosol concentrations, aerosol size distributions, vertical profiles of air temperature, diurnal wind cycles, and spatio-temporal rainfall patterns. Rainfall over the Red Sea coast is mainly caused by warm rain processes, which are typically confined within a height of ~6 km over the Sarawat mountains and exhibit a strong diurnal cycle that peaks in the evening at approximately 18:00 local time under the influence of sea breezes. Numerical experiments indicated that dust could both suppress or enhance rainfall. The effect of dust on rainfall was calculated as total, indirect, and direct effects, based on 10-year August-average daily-accumulated rainfall over the study domain covering the eastern Red Sea coast. For extreme rainfall events (domain-average daily-accumulated rainfall of ≥ 1.33 mm), the net effect of dust on rainfall was positive or enhancement (6.05 %), with the indirect effect (4.54 %) and direct effect (1.51 %) both causing rainfall increase. At a 5 % significance level, the total and indirect effects were statistically significant whereas the direct effect was not. For normal rainfall events (domain-average daily-accumulated rainfall < 1.33 mm), the indirect effect enhanced rainfall (4.76 %) whereas the direct effect suppressed rainfall (-5.78 %), resulting in a negative net suppressing effect (-1.02 %), all of which were statistically significant. We investigated the possible physical mechanisms of the effects and found that the rainfall suppression by dust direct effects was mainly caused by the scattering of solar radiation by dust. The surface cooling induced by dust weakens the sea breeze circulation, which decreases the associated landward moisture transport, ultimately suppressing rainfall. For extreme rainfall events, dust causes net rainfall enhancement through indirect effects as the high dust concentration facilitates raindrops to grow when the water vapor is sufficiently available. Our results have broader scientific and environmental implications. Specifically, although dust is considered a problem from an air quality perspective, our results highlight the important role of dust on sea breeze circulation and associated rainfall over the Red Sea coastal regions. Our results also have implications for cloud seeding and water resource management.
Highlights: • Rainfall erosivity for 10 rainfall stations located in Slovenia was evaluated. • Rainfall erosivity trends are mostly positive while mostly statistically insignificant. • The maximum rainfall erosivity is obtained under Alpine climate. • Monthly rainfall erosivity was computed. • Sensitivity analyses were carried out. Slovenia is one of the EU countries with the largest values and largest amounts of variability in rainfall erosivity, with maximum annual values exceeding 10,000 MJ mm ha{sup -1} h{sup -1} yr{sup -1}. Five-minute rainfall data was analysed from 10 Slovenian rainfall stations with data-length availability longer than 25 years with a maximum data length of 69 years and a total data-station length equal to 443 years. Trends in the rainfall erosivity R-factor were detected for four different sub-samples using monthly, half-year, and annual rainfall erosivity values. The results indicate that rainfall erosivity trends for the selected Slovenian stations are mostly statistically insignificant, with the selected significance level of 0.05. However, a larger share of identified trends are positive than negative. The maximum annual rainfall erosivity values were obtained for one specific mountain station. Furthermore, a sensitivity analysis regarding the rainfall erosivity factor R calculation showed that the rainfall threshold parameter (12.7 mm) that is used to remove the small-magnitude rainfall events in order to reduce the computational burden can attribute up to 10% of the average annual R-values in cases where this threshold is not used. Other parameters have, on average, a smaller impact on the calculated rainfall erosivity. Furthermore, the application of local kinetic energy equations resulted in, on average, about 20% higher annual rainfall erosivity values compared to the equation that is proposed by the Revised Universal Soil Loss Equation (RUSLE) manual and was not developed specifically for this region.
Central Florida is the ideal test laboratory for studying convergence zone-induced convection. The region regularly experiences sea breeze fronts and rainfall-induced outflow boundaries. The focus of this study is the common yet poorly-studied convergence zone established by the interaction of the sea breeze front and an outflow boundary. Previous studies have investigated mechanisms primarily affecting storm initiation by such convergence zones. Few have focused on rainfall morphology yet these storms contribute a significant amount precipitation to the annual rainfall budget. Low-level convergence and mid-tropospheric moisture have both been shown to correlate with rainfall amounts in Florida. Using 2D and 3D numerical simulations, the roles of low-level convergence and mid-tropospheric moisture in rainfall evolution are examined. The results indicate that time-averaged, vertical moisture flux (VMF) at the sea breeze front/outflow convergence zone is directly and linearly proportional to initial condensation rates. This proportionality establishes a similar relationship between VMF and initial rainfall. Vertical moisture flux, which encompasses depth and magnitude of convergence, is better correlated to initial rainfall production than surface moisture convergence. This extends early observational studies which linked rainfall in Florida to surface moisture convergence. The amount and distribution of mid-tropospheric moisture determines how rainfall associated with secondary cells develop. Rainfall amount and efficiency varied significantly over an observable range of relative humidities in the 850- 500 mb layer even though rainfall evolution was similar during the initial or "first-cell" period. Rainfall variability was attributed to drier mid-tropospheric environments inhibiting secondary cell development through entrainment effects. Observationally, 850-500 mb moisture structure exhibits wider variability than lower level moisture, which is virtually always present in Florida. A likely consequence of the variability in 850-500 moisture is a stronger statistical correlation to rainfall, which observational studies have noted. The study indicates that vertical moisture flux forcing at convergence zones is critical in determining rainfall in the initial stage of development but plays a decreasing role in rainfall evolution as the system matures. The mid-tropospheric moisture (e.g. environment) plays an increasing role in rainfall evolution as the system matures. This suggests the need to improve measurements of magnitude/depth of convergence and mid-tropospheric moisture distribution. It also highlights the need for better parameterization of entrainment and vertical moisture distribution in larger-scale models.
The space-time features of rainfall diurnal variation of precipitation are systematically investigated by using the Tropical Rainfall Measuring Mission (TRMM) precipitation products retrieved from TRMM microwave imager (TMI), precipitation radar (PR) and TMI/PR combined algorithms. Results demonstrate that diurnal variability of precipitation is obvious over tropical regions. The dominant feature of rainfall diurnal cycle over, ocean is that there is consistent rainfall peak in early morning, while there is a consistent rainfall peak in mid-late afternoon over land. The seasonal variation on intensity of rainfall diurnal cycle is clearly evidenced. Horizontal distributions of rainfall diurnal variations indicate that there is a clearly early-morning peak with a secondary peak in the middle-late afternoon in ocean rainfall at latitudes dominated by large-scale convergence and deep convection. There is also an analogous early-morning peak in land rainfall along with a stronger afternoon peak forced by surface heating. Amplitude analysis shows that the patterns and its evolution of rainfall diurnal cycle are very close to rainfall distribution pattern and its evolution. These results indicate that rainfall diurnal variations are strongly associated with large-scale convective systems and climate weather systems. Phase studies clearly present the regional and seasonal features of rainfall diurnal activities. Further studies on convective and stratiform rainfall show different characteristics of diurnal cycles. Their spatial and temporal variations of convective and stratiform rainfall indicate that mechanisms for rainfall diurnal variations vary with time and space.
Models for forecasting rainfall-induced landslides are mostly based on the identification of empirical rainfall thresholds obtained exploiting rain gauge data. Despite their increased availability, satellite rainfall estimates are scarcely used for this purpose. Satellite data should be useful in ungauged and remote areas, or should provide a significant spatial and temporal reference in gauged areas. In this paper, the analysis of the reliability of rainfall thresholds based on rainfall remote sensed and rain gauge data for the prediction of landslide occurrence is carried out. To date, the estimation of the uncertainty associated with the empirical rainfall thresholds is mostly based on a bootstrap resampling of the rainfall duration and the cumulated event rainfall pairs (D,E) characterizing rainfall events responsible for past failures. This estimation does not consider the measurement uncertainty associated with D and E. In the paper, we propose (i) a new automated procedure to reconstruct ED conditions responsible for the landslide triggering and their uncertainties, and (ii) three new methods to identify rainfall threshold for the possible landslide occurrence, exploiting rain gauge and satellite data. In particular, the proposed methods are based on Least Square (LS), Quantile Regression (QR) and Nonlinear Least Square (NLS) statistical approaches. We applied the new procedure and methods to define empirical rainfall thresholds and their associated uncertainties in the Umbria region (central Italy) using both rain-gauge measurements and satellite estimates. We finally validated the thresholds and tested the effectiveness of the different threshold definition methods with independent landslide information. The NLS method among the others performed better in calculating thresholds in the full range of rainfall durations. We found that the thresholds obtained from satellite data are lower than those obtained from rain gauge measurements. This is in agreement with the literature, where satellite rainfall data underestimate the 'ground' rainfall registered by rain gauges.
Land–atmosphere interactions play an important role in summer rainfall in the central United States, where mesoscale convective systems (MCSs) contribute to 30 to 70% of warm-season precipitation. Previous studies of soil moisture–precipitation feedbacks focused on the total precipitation, confounding the distinct roles of rainfall from different convective storm types. Here, we investigate the soil moisture–precipitation feedbacks associated with MCS and non-MCS rainfall and their surface hydrological footprints using a unique combination of these rainfall events in observations and land surface simulations with numerical tracers to quantify soil moisture sourced from MCS and non-MCS rainfall. Furthermore, we find that early warm-season (April to June) MCS rainfall, which is characterized by higher intensity and larger area per storm, produces coherent mesoscale spatial heterogeneity in soil moisture that is important for initiating summer (July) afternoon rainfall dominated by non-MCS events. On the other hand, soil moisture sourced from both early warm-season MCS and non-MCS rainfall contributes to lower-level atmospheric moistening favorable for upscale growth of MCSs at night. However, soil moisture sourced from MCS rainfall contributes to July MCS rainfall with a longer lead time because with higher intensity, MCS rainfall percolates into deeper soil that has a longer memory. Therefore, early warm-season MCS rainfall dominates soil moisture–precipitation feedback. This motivates future studies to examine the contribution of early warm-season MCS rainfall and associated soil moisture anomalies to predictability of summer rainfall in the major agricultural region of the central United States and other continental regions frequented by MCSs.
Abstract The influence of the boreal summer intraseasonal oscillation (BSISO) on the diurnal cycle of coastal rainfall over south China during the mei-yu (heavy rainfall) season is investigated using the OLR-based Madden–Julian oscillation index (OMI), satellite rainfall data, and atmospheric reanalysis. Results show that the mei-yu season coastal rainfall is enhanced during the BSISO phase 1 (convectively active phase over the western Indian Ocean), with 25% greater rainfall than the climatological regional mean. Rainfall is suppressed during the BSISO phases 4 and 5 (convectively active phase in the Bay of Bengal and South China Sea), with negative rainfall anomalies of 39% and 46%, respectively. During phase 1, the rainfall enhancement is mostly over the inland region during the afternoon, while there is little diurnal variability of the rainfall anomaly offshore. During phases 4 and 5, the rainfall suppression is considerably stronger over the offshore region in the morning, whereas stronger rainfall suppression occurs inland during the afternoon. In phase 8, positive rainfall anomalies are found over the offshore region with a peak from the morning to the early afternoon, whereas negative rainfall anomalies are found over the inland region with the strongest suppression in the late afternoon. Analysis of phase composites and horizontal moisture advection shows that the diurnal variation of rainfall anomalies over the south China coastal area during different BSISO phases can be interpreted as the interaction between the large-scale anomalous moisture advection and the local land and sea breeze circulations.
Warm-season rainfall associated with mesoscale convective systems (MCSs) in the central US is characterized by higher intensity and nocturnal timing compared to rainfall from non-MCS systems, suggesting their potentially different footprints on the land surface. To differentiate the impacts of MCS and non-MCS rainfall on the surface water balance, a water tracer tool embedded in the Noah land surface model with multi-parameterization options (WT-Noah-MP) is used to numerically “tag” water from MCS and non-MCS rainfall separately during April to August (1997-2018) and track their transit in the terrestrial system. From the water-tagging results, over 50% of warm-season rainfall leaves the surface-subsurface system through evapotranspiration by the end of August, but non-MCS rainfall contributes a larger fraction. However, MCS rainfall plays a more important role in generating surface runoff. These differences are mostly attributed to the rainfall intensity differences. The higher intensity MCS rainfall tends to produce more surface runoff through infiltration excess flow and drives a deeper penetration of the rainwater into the soil. Over 70% of the top 10 percentile runoff is contributed by MCS rainfall, demonstrating its important contribution to local flooding. In contrast, lighter intensity non-MCS rainfall resides mostly in the top layer and contributes more to evapotranspiration through soil evaporation. Diurnal timing of rainfall has negligible effects on the flux partitioning for both MCS and non-MCS rainfall. Differences in soil moisture profiles for MCS and non-MCS rainfall and the resultant evapotranspiration suggest differences in their roles in soil moisture-precipitation feedbacks and ecohydrology.
The tropical cyclone rainfall climatology study that was performed for the North Pacific was extended to the North Atlantic. Similar to the North Pacific tropical cyclone study, mean monthly rainfall within 444 km of the center of the North Atlantic tropical cyclones (i.e., that reached storm stage and greater) was estimated from passive microwave satellite observations during, an eleven year period. These satellite-observed rainfall estimates were used to assess the impact of tropical cyclone rainfall in altering the geographical, seasonal, and inter-annual distribution of the North Atlantic total rainfall during, June-November when tropical cyclones were most abundant. The main results from this study indicate: 1) that tropical cyclones contribute, respectively, 4%, 3%, and 4% to the western, eastern, and entire North Atlantic; 2) similar to that observed in the North Pacific, the maximum in North Atlantic tropical cyclone rainfall is approximately 5 - 10 deg poleward (depending on longitude) of the maximum non-tropical cyclone rainfall; 3) tropical cyclones contribute regionally a maximum of 30% of the total rainfall 'northeast of Puerto Rico, within a region near 15 deg N 55 deg W, and off the west coast of Africa; 4) there is no lag between the months with maximum tropical cyclone rainfall and non-tropical cyclone rainfall in the western North Atlantic, while in the eastern North Atlantic, maximum tropical cyclone rainfall precedes maximum non-tropical cyclone rainfall; 5) like the North Pacific, North Atlantic tropical cyclones Of hurricane intensity generate the greatest amount of rainfall in the higher latitudes; and 6) warm ENSO events inhibit tropical cyclone rainfall.
Extreme monsoon rainfall in India has disastrous consequences, including significant socio- economic impacts. However, little is known about the overall trends and climate factors associated with extreme rainfall because rainfall greatly varies across India and because few appropriate methods are available to measure extreme rainfall in the context of such heterogeneity. To provide a comprehensive assessment of extreme monsoon rainfall, we developed a metric using record rainfall data to measure the changes in the likelihood of extreme high and extreme low rainfall over time; this metric is independent of the characteristics of the underlying rainfall distributions. Hence, the metric is ideally suited to aggregate extreme rainfall information across heterogeneous regions covering India. We found that from 1930 to 2013, the likelihood of extreme high and extreme low rainfall increases 2-fold and 4-fold, respectively. These overall trend increases are driven by anomalous increases, particularly in the early 2000s; the likelihood of extreme high and extreme low rainfall increases 5-fold and 18-fold in 2005 and 2002, respectively. These findings imply a broadening of the underlying monsoon rainfall distribution over the past century. We also show that the time patterns of the likelihood of extreme rainfall in recent decades are correlated with the El Nino Southern Oscillation, Indian Ocean Dipole, and surface air temperature in the Northern Hemisphere.
Warm-season rainfall characteristics in the central US are investigated as they play important roles in ecohydrology and agricultural productivity. Using rainfall observations, we compare the April-August mesoscale convective systems (MCS) and non-MCS rainfall characteristics and examine their linear trends between 1997 and 2018. We found that MCS rainfall is ~7 times more intense than non-MCS rainfall but it occurs less frequently in time and space. MCS rainfall peaks in nocturnal hours, with synchronized timing of rainfall intensity, area and occurrence, while non-MCS rainfall peaks in late-afternoon hours, mostly attributed to the timing of peak rainfall area. MCS rainfall has increased in the last 22 years due to an increase in frequency and a longer duration per MCS. In contrast, non-MCS rainfall has decreased mainly due to a reduction in rainfall area, leading to fewer total wet days and increased dry intervals between storms