Decadal-scale rainfall variability in Ethiopia recorded in an annually laminated, Holocene-age, stalagmite

An annually laminated, uranium-series dated, Holocene stalagmite from southeast Ethiopia has been analysed for growth rate and δ13C and δ18O variations at annual to biennial resolution, in order to provide the first long duration proxy record of decadal-scale rainfall variability in this climatically sensitive region. Our study site (10°N) is climatically influenced by both summer (June—August) and spring (March—May) rainfall caused by the annual movement of the Inter-Tropical Convergence Zone (ITCZ) and modulated by large-scale anomalies in the atmospheric circulation and in ocean temperatures. Here we show that stalagmite growth, episodic throughout the last 7800 years, demonstrates decadal-scale (8—25 yr) variability in both growth rate and δ 18O. A hydrological model was employed and indicates that this decadal variability is due to variations in the relative amounts of rainfall in the two rain seasons. Our record, unique in its combination of length (a total of ~1000 years), annual chronology and high resolution δ18O, shows for the first time that such decadal-scale variability in rainfall in this region has occurred through the Holocene, which implies persistent decadal-scale variability for the large-scale atmospheric and oceanic driving factors.

Determining the influence of rainfall patterns and carbendazim on the surface activity of the earthworm Lumbricus terrestris

Carbendazim is highly toxic to earthworms and is used as a standard control substance when running field-based trials of pesticides, but results using carbendazim are highly variable. In the present study, impacts of timing of rainfall events following carbendazim application on earthworms were investigated. Lumbricus terrestris were maintained in soil columns to which carbendazim and then deionized water (a rainfall substitute) were applied. Carbendazim was applied at 4 kg/ha, the rate recommended in pesticide field trials. Three rainfall regimes were investigated: initial and delayed heavy rainfall 24 h and 6 d after carbendazim application, and frequent rainfall every 48 h. Earthworm mortality and movement of carbendazim through the soil was assessed 14 d after carbendazim application. No detectable movement of carbendazim occurred through the soil in any of the treatments or controls. Mortality in the initial heavy and frequent rainfall was significantly higher (approximately 55%) than in the delayed rainfall treatment (approximately 25%). This was due to reduced bioavailability of carbendazim in the latter treatment due to a prolonged period of sorption of carbendazim to soil particles before rainfall events. The impact of carbendazim application on earthworm surface activity was assessed using video cameras. Carbendazim applications significantly reduced surface activity due to avoidance behavior of the earthworms. Surface activity reductions were least in the delayed rainfall treatment due to the reduced bioavailability of the carbendazim. The nature of rainfall events' impacts on the response of earthworms to carbendazim applications, and details of rainfall events preceding and following applications during field trials should be made at a higher level of resolution than is currently practiced according to standard International Organization for Standardization protocols.

Maize yield and rainfall on different spatial and temporal scales in Southern Brazil

This study aimed to establish relationships between maize yield and rainfall on different temporal and spatial scales, in order to provide a basis for crop monitoring and modelling. A 16-year series of maize yield and daily rainfall from 11 municipalities and micro-regions of Rio Grande do Sul State was used. Correlation and regression analyses were used to determine associations between crop yield and rainfall for the entire crop cycle, from tasseling to 30 days after, and from 5 days before tasseling to 40 days after. Close relationships between maize yield and rainfall were found, particularly during the reproductive period (45-day period comprising the flowering and grain filling). Relationships were closer on a regional scale than at smaller scales. Implications of the crop-rainfall relationships for crop modelling are discussed.

Effects of summer rainfall manipulations on the abundance and vertical distribution of herbivorous soil macro-invertebrates

Soil invertebrate communities are likely to be highly vulnerable to low soil moisture, caused by a reduction in summer rainfall which is predicted for some regions under current climate change scenarios. However, the effects of changes in summer rainfall on soil invertebrate assemblages have rarely been tested experimentally. In this study, samples were taken in 2003 and 2004 from a long-running field experiment, to investigate the impact of 10 years of experimental summer drought and increased summer rainfall manipulations on the soil fauna of a calcareous grassland. Summer drought altered the soil invertebrate assemblage in the autumn, immediately following treatment application, but by the following spring treatment effects were no longer apparent. The two most common root herbivore species responded differently to the summer rainfall manipulations. Larvae of the dominant root-chewing species, Agriotes lineatus, were more numerous under enhanced rainfall in both the spring and autumn. In contrast, abundance of the Coccoidea Lecanopsis formicarum was unaffected by the rainfall manipulations. The responses of root herbivores to an increased incidence of summer droughts are therefore likely to vary, depending on their feeding strategy and life history. (c) 2007 Elsevier Masson SAS. All rights reserved.

Multiple-scattering in radar systems: a review

Although extensively studied within the lidar community, the multiple scattering phenomenon has always been considered a rare curiosity by radar meteorologists. Up to few years ago its appearance has only been associated with two- or three-body-scattering features (e.g. hail flares and mirror images) involving highly reflective surfaces. Recent atmospheric research aimed at better understanding of the water cycle and the role played by clouds and precipitation in affecting the Earth's climate has driven the deployment of high frequency radars in space. Examples are the TRMM 13.5 GHz, the CloudSat 94 GHz, the upcoming EarthCARE 94 GHz, and the GPM dual 13-35 GHz radars. These systems are able to detect the vertical distribution of hydrometeors and thus provide crucial feedbacks for radiation and climate studies. The shift towards higher frequencies increases the sensitivity to hydrometeors, improves the spatial resolution and reduces the size and weight of the radar systems. On the other hand, higher frequency radars are affected by stronger extinction, especially in the presence of large precipitating particles (e.g. raindrops or hail particles), which may eventually drive the signal below the minimum detection threshold. In such circumstances the interpretation of the radar equation via the single scattering approximation may be problematic. Errors will be large when the radiation emitted from the radar after interacting more than once with the medium still contributes substantially to the received power. This is the case if the transport mean-free-path becomes comparable with the instrument footprint (determined by the antenna beam-width and the platform altitude). This situation resembles to what has already been experienced in lidar observations, but with a predominance of wide- versus small-angle scattering events. At millimeter wavelengths, hydrometeors diffuse radiation rather isotropically compared to the visible or near infrared region where scattering is predominantly in the forward direction. A complete understanding of radiation transport modeling and data analysis methods under wide-angle multiple scattering conditions is mandatory for a correct interpretation of echoes observed by space-borne millimeter radars. This paper reviews the status of research in this field. Different numerical techniques currently implemented to account for higher order scattering are reviewed and their weaknesses and strengths highlighted. Examples of simulated radar backscattering profiles are provided with particular emphasis given to situations in which the multiple scattering contributions become comparable or overwhelm the single scattering signal. We show evidences of multiple scattering effects from air-borne and from CloudSat observations, i.e. unique signatures which cannot be explained by single scattering theory. Ideas how to identify and tackle the multiple scattering effects are discussed. Finally perspectives and suggestions for future work are outlined. This work represents a reference-guide for studies focused at modeling the radiation transport and at interpreting data from high frequency space-borne radar systems that probe highly opaque scattering media such as thick ice clouds or precipitating clouds.

Using continuous ground-based radar and lidar measurements for evaluating the representation of clouds in four operational models

The ability of four operational weather forecast models [ECMWF, Action de Recherche Petite Echelle Grande Echelle model (ARPEGE), Regional Atmospheric Climate Model (RACMO), and Met Office] to generate a cloud at the right location and time (the cloud frequency of occurrence) is assessed in the present paper using a two-year time series of observations collected by profiling ground-based active remote sensors (cloud radar and lidar) located at three different sites in western Europe (Cabauw. Netherlands; Chilbolton, United Kingdom; and Palaiseau, France). Particular attention is given to potential biases that may arise from instrumentation differences (especially sensitivity) from one site to another and intermittent sampling. In a second step the statistical properties of the cloud variables involved in most advanced cloud schemes of numerical weather forecast models (ice water content and cloud fraction) are characterized and compared with their counterparts in the models. The two years of observations are first considered as a whole in order to evaluate the accuracy of the statistical representation of the cloud variables in each model. It is shown that all models tend to produce too many high-level clouds, with too-high cloud fraction and ice water content. The midlevel and low-level cloud occurrence is also generally overestimated, with too-low cloud fraction but a correct ice water content. The dataset is then divided into seasons to evaluate the potential of the models to generate different cloud situations in response to different large-scale forcings. Strong variations in cloud occurrence are found in the observations from one season to the same season the following year as well as in the seasonal cycle. Overall, the model biases observed using the whole dataset are still found at seasonal scale, but the models generally manage to well reproduce the observed seasonal variations in cloud occurrence. Overall, models do not generate the same cloud fraction distributions and these distributions do not agree with the observations. Another general conclusion is that the use of continuous ground-based radar and lidar observations is definitely a powerful tool for evaluating model cloud schemes and for a responsive assessment of the benefit achieved by changing or tuning a model cloud

Evaluating forecasts of the evolution of the cloudy boundary layer using diurnal composites of radar and lidar observations

Observations of boundary-layer cloud have been made using radar and lidar at Chilbolton, Hampshire, UK. These have been compared with output from 7 different global and regional models. Fifty-five cloudy days have been composited to reveal the mean diurnal variation of cloud top and base heights, cloud thickness and liquid water path of the clouds. To enable like-for-like comparison between model and observations, the observations have been averaged on to the grid of each model. The composites show a distinct diurnal cycle in observed cloud; the cloud height exhibits a sinusoidal variation throughout the day with a maximum at around 1600 and a minimum at around 0700 UTC. This diurnal cycle is captured by six of the seven models analysed, although the models generally under-predict both cloud top and cloud base heights throughout the day. The two worst performing models in terms of cloud boundaries also have biases of around a factor of two in liquid water path; these were the only two models that did not include an explicit formulation for cloud-top entrainment.

Precipitation and latent heating distributions from satellite passive microwave radiometry. Part I: improved method and uncertainties

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5°-resolution range from approximately 50% at 1 mm h−1 to 20% at 14 mm h−1. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%–80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5° resolution is relatively small (less than 6% at 5 mm day−1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%–35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%–15% at 5 mm day−1, with proportionate reductions in latent heating sampling errors.

Bayesian retrieval of complete posterior PDFs of oceanic rain rate from microwave observations

A new Bayesian algorithm for retrieving surface rain rate from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) over the ocean is presented, along with validations against estimates from the TRMM Precipitation Radar (PR). The Bayesian approach offers a rigorous basis for optimally combining multichannel observations with prior knowledge. While other rain-rate algorithms have been published that are based at least partly on Bayesian reasoning, this is believed to be the first self-contained algorithm that fully exploits Bayes’s theorem to yield not just a single rain rate, but rather a continuous posterior probability distribution of rain rate. To advance the understanding of theoretical benefits of the Bayesian approach, sensitivity analyses have been conducted based on two synthetic datasets for which the “true” conditional and prior distribution are known. Results demonstrate that even when the prior and conditional likelihoods are specified perfectly, biased retrievals may occur at high rain rates. This bias is not the result of a defect of the Bayesian formalism, but rather represents the expected outcome when the physical constraint imposed by the radiometric observations is weak owing to saturation effects. It is also suggested that both the choice of the estimators and the prior information are crucial to the retrieval. In addition, the performance of the Bayesian algorithm herein is found to be comparable to that of other benchmark algorithms in real-world applications, while having the additional advantage of providing a complete continuous posterior probability distribution of surface rain rate.

Using Doppler radar with a simple explicit microphysics model to diagnose problems with ice sublimation depth-scales in forecast models

Several previous studies have attempted to assess the sublimation depth-scales of ice particles from clouds into clear air. Upon examining the sublimation depth-scales in the Met Office Unified Model (MetUM), it was found that the MetUM has evaporation depth-scales 2–3 times larger than radar observations. Similar results can be seen in the European Centre for Medium-Range Weather Forecasts (ECMWF), Regional Atmospheric Climate Model (RACMO) and Météo-France models. In this study, we use radar simulation (converting model variables into radar observations) and one-dimensional explicit microphysics numerical modelling to test and diagnose the cause of the deep sublimation depth-scales in the forecast model. The MetUM data and parametrization scheme are used to predict terminal velocity, which can be compared with the observed Doppler velocity. This can then be used to test the hypothesis as to why the sublimation depth-scale is too large within the MetUM. Turbulence could lead to dry air entrainment and higher evaporation rates; particle density may be wrong, particle capacitance may be too high and lead to incorrect evaporation rates or the humidity within the sublimating layer may be incorrectly represented. We show that the most likely cause of deep sublimation zones is an incorrect representation of model humidity in the layer. This is tested further by using a one-dimensional explicit microphysics model, which tests the sensitivity of ice sublimation to key atmospheric variables and is capable of including sonde and radar measurements to simulate real cases. Results suggest that the MetUM grid resolution at ice cloud altitudes is not sufficient enough to maintain the sharp drop in humidity that is observed in the sublimation zone.

3D-Var assimilation of insect-derived Doppler radar radial winds in convective cases using a high resolution model

The assimilation of Doppler radar radial winds for high resolution NWP may improve short term forecasts of convective weather. Using insects as the radar target, it is possible to provide wind observations during convective development. This study aims to explore the potential of these new observations, with three case studies. Radial winds from insects detected by 4 operational weather radars were assimilated using 3D-Var into a 1.5 km resolution version of the Met Office Unified Model, using a southern UK domain and no convective parameterization. The effect on the analysis wind was small, with changes in direction and speed up to 45° and 2 m s−1 respectively. The forecast precipitation was perturbed in space and time but not substantially modified. Radial wind observations from insects show the potential to provide small corrections to the location and timing of showers but not to completely relocate convergence lines. Overall, quantitative analysis indicated the observation impact in the three case studies was small and neutral. However, the small sample size and possible ground clutter contamination issues preclude unequivocal impact estimation. The study shows the potential positive impact of insect winds; future operational systems using dual polarization radars which are better able to discriminate between insects and clutter returns should provided a much greater impact on forecasts.

Rapid topographic change measured by high-resolution satellite radar at Soufriere Hills Volcano, Montserrat, 2008-2010

High-resolution satellite radar observations of erupting volcanoes can yield valuable information on rapidly changing deposits and geomorphology. Using the TerraSAR-X (TSX) radar with a spatial resolution of about 2 m and a repeat interval of 11-days, we show how a variety of techniques were used to record some of the eruptive history of the Soufriere Hills Volcano, Montserrat between July 2008 and February 2010. After a 15-month pause in lava dome growth, a vulcanian explosion occurred on 28 July 2008 whose vent was hidden by dense cloud. We were able to show the civil authorities using TSX change difference images that this explosion had not disrupted the dome sufficient to warrant continued evacuation. Change difference images also proved to be valuable in mapping new pyroclastic flow deposits: the valley-occupying block-and-ash component tending to increase backscatter and the marginal surge deposits reducing it, with the pattern reversing after the event. By comparing east- and west-looking images acquired 12 hours apart, the deposition of some individual pyroclastic flows can be inferred from change differences. Some of the narrow upper sections of valleys draining the volcano received many tens of metres of rockfall and pyroclastic flow deposits over periods of a few weeks. By measuring the changing shadows cast by these valleys in TSX images the changing depth of infill by deposits could be estimated. In addition to using the amplitude data from the radar images we also used their phase information within the InSAR technique to calculate the topography during a period of no surface activity. This enabled areas of transient topography, crucial for directing future flows, to be captured.

Climatic and oceanic associations with daily rainfall extremes over southern Africa

Changes in climate variability and, in particular, changes in extreme climate events are likely to be of far more significance for environmentally vulnerable regions than changes in the mean state. It is generally accepted that sea-surface temperatures (SSTs) play an important role in modulating rainfall variability. Consequently, SSTs can be prescribed in global and regional climate modelling in order to study the physical mechanisms behind rainfall and its extremes. Using a satellite-based daily rainfall historical data set, this paper describes the main patterns of rainfall variability over southern Africa, identifies the dates when extreme rainfall occurs within these patterns, and shows the effect of resolution in trying to identify the location and intensity of SST anomalies associated with these extremes in the Atlantic and southwest Indian Ocean. Derived from a Principal Component Analysis (PCA), the results also suggest that, for the spatial pattern accounting for the highest amount of variability, extremes extracted at a higher spatial resolution do give a clearer indication regarding the location and intensity of anomalous SST regions. As the amount of variability explained by each spatial pattern defined by the PCA decreases, it would appear that extremes extracted at a lower resolution give a clearer indication of anomalous SST regions.