Ensemble representation of uncertainty in Lagrangian satellite rainfall estimates

A new algorithm called Lagrangian Simulation (LSIM) has been developed that enables the interpolation uncertainty present in Lagrangian satellite rainfall algorithms such as the Climate Prediction Center (CPC) morphing technique (CMORPH) to be characterized using an ensemble product. The new algorithm generates ensemble sequences of rainfall fields conditioned on multiplatform multisensor microwave satellite data, demonstrating a conditional simulation approach that overcomes the problem of discontinuous uncertainty fields inherent in this type of product. Each ensemble member is consistent with the information present in the satellite data, while variation between members is indicative of uncertainty in the rainfall retrievals. LSIM is based on the combination of a Markov weather generator, conditioned on both previous and subsequent microwave measurements, and a global optimization procedure that uses simulated annealing to constrain the generated rainfall fields to display appropriate spatial structures. The new algorithm has been validated over a region of the continental United States and has been shown to provide reliable estimates of both point uncertainty distributions and wider spatiotemporal structures

LMODEL: A satellite precipitation methodology using cloud development modeling. Part I: Algorithm construction and calibration

The Lagrangian Model (LMODEL) is a new multisensor satellite rainfall monitoring methodology based on the use of a conceptual cloud-development model that is driven by geostationary satellite imagery and is locally updated using microwave-based rainfall measurements from low earth-orbiting platforms. This paper describes the cloud development model and updating procedures; the companion paper presents model validation results. The model uses single-band thermal infrared geostationary satellite imagery to characterize cloud motion, growth, and dispersal at high spatial resolution (similar to 4 km). These inputs drive a simple, linear, semi-Lagrangian, conceptual cloud mass balance model, incorporating separate representations of convective and stratiform processes. The model is locally updated against microwave satellite data using a two-stage process that scales precipitable water fluxes into the model and then updates model states using a Kalman filter. Model calibration and updating employ an empirical rainfall collocation methodology designed to compensate for the effects of measurement time difference, geolocation error, cloud parallax, and rainfall shear

Blue Nile Runoff Sensitivity to Climate Change

This study describes implementation of hydrological climate change impact assessment tool utilising a combination of statistical spatiotemporal downscaling and an operational hydrological model known as the Nile Forecasting System. A spatial rainfall generator was used to produce high-resolution (daily, 20km) gridded rainfall data required by the distributed hydrological model from monthly GCM outputs. The combined system was used to assess the sensitivity of upper Blue Nile flows at Diem flow gauging station to changes in future rainfall during the June-September rainy season based on output from three GCMs. The assessment also incorporated future evapotranspiration changes over the basin. The climate change scenarios derived in this study were broadly in line with other studies, with the majority of scenarios indicating wetter conditions in the future. Translating the impacts into runoff in the basin showed increased future mean flows, although these would be offset to some degree by rising evapotranspiration. Impacts on extreme runoff indicated the possibility of more severe floods in future. These are likely to be exacerbated by land-use changes including overgrazing, deforestation, and improper farming practices. Blue Nile basin flood managers therefore need to continue to prepare for the possibility of more frequent floods by adopting a range of measures to minimise loss of life and guard against other flood damage

WRF model sensitivity to choice of parameterization: a study of the 'York Flood 1999'

© 2014, Springer-Verlag Wien. Numerical weather modelling has gained considerable attention in the field of hydrology especially in un-gauged catchments and in conjunction with distributed models. As a consequence, the accuracy with which these models represent precipitation, sub-grid-scale processes and exceptional events has become of considerable concern to the hydrological community. This paper presents sensitivity analyses for the Weather Research Forecast (WRF) model with respect to the choice of physical parameterization schemes (both cumulus parameterisation (CPSs) and microphysics parameterization schemes (MPSs)) used to represent the ‘1999 York Flood’ event, which occurred over North Yorkshire, UK, 1 st –14 th March 1999. The study assessed four CPSs (Kain–Fritsch (KF2), Betts–Miller–Janjic (BMJ), Grell–Devenyi ensemble (GD) and the old Kain–Fritsch (KF1)) and four MPSs (Kessler, Lin et al., WRF single-moment 3-class (WSM3) and WRF single-moment 5-class (WSM5)] with respect to their influence on modelled rainfall. The study suggests that the BMJ scheme may be a better cumulus parameterization choice for the study region, giving a consistently better performance than other three CPSs, though there are suggestions of underestimation. The WSM3 was identified as the best MPSs and a combined WSM3/BMJ model setup produced realistic estimates of precipitation quantities for this exceptional flood event. This study analysed spatial variability in WRF performance through categorical indices, including POD, FBI, FAR and CSI during York Flood 1999 under various model settings. Moreover, the WRF model was good at predicting high-intensity rare events over the Yorkshire region, suggesting it has potential for operational use

Seasonal variation in marine C:N:P stoichiometry: can the composition of seston explain stable Redfield ratios?

Seston is suspended particulate organic matter, comprising a mixture of autotrophic, heterotrophic and detrital material. Despite variable proportions of these components, marine seston often exhibits relatively small deviations from the Redfield ratio (C:N:P = 106:16:1). Two time-series from the Norwegian shelf in Skagerrak are used to identify drivers of the seasonal variation in seston elemental ratios. An ordination identified water mass characteristics and bloom dynamics as the most important drivers for determining C:N, while changes in nutrient concentrations and biomass were most important for the C:P and N:P relationships. There is no standardized method for determining the functional composition of seston and the fractions of POC, PON and PP associated with phytoplankton, therefore any such information has to be obtained by indirect means. In this study, a generalized linear model was used to differentiate between the live autotrophic and non-autotrophic sestonic fractions, and for both stations the non-autotrophic fractions dominated with respective annual means of 76 and 55%. This regression model approach builds on assumptions (e.g. constant POC:Chl-a ratio) and the robustness of the estimates were explored with a bootstrap analysis. In addition the autotrophic percentage calculated from the statistical model was compared with estimated phytoplankton carbon, and the two independent estimates of autotrophic percentage were comparable with similar seasonal cycles. The estimated C:nutrient ratios of live autotrophs were, in general, lower than Redfield, while the non-autotrophic C:nutrient ratios were higher than the live autotrophic ratios and above, or close to, the Redfield ratio. This is due to preferential remineralization of nutrients, and the P content mainly governed the difference between the sestonic fractions. Despite the seasonal variability in seston composition and the generally low contribution of autotrophic biomass, the variation observed in the total seston ratios was low compared to the variation found in dissolved and particulate pools. Sestonic C:N:P ratios close to the Redfield ratios should not be used as an indicator of phytoplankton physiological state, but could instead reflect varying contributions of sestonic fractions that sum up to an elemental ratio close to Redfield

Variation in the seston C:N ratio of the Arctic Ocean and pan-Arctic shelves

Studying more than 3600 observations of particulate organic carbon (POC) and particulate organic nitrogen (PON), we evaluate the applicability of the classic Redfield C:N ratio (6.6) and the recently proposed Sterner ratio (8.3) for the Arctic Ocean and pan-Arctic shelves. The confidence intervals for C:N ranged from 6.43 to 8.82, while the average C:N ratio for all observations was 7.4. In general, neither the Redfield or Sterner ratios were applicable, with the Redfield ratio being too low and the Sterner ratio too high. On a regional basis, all northern high latitude regions had a C:N ratio significantly higher than the Redfield ratio, except the Arctic Ocean (6.6), Chukchi (6.4) and East Siberian (6.5) Seas. The latter two regions were influenced by nutrient-rich Pacific waters, and had a high fraction of autotrophic (i.e. algal-derived) material. The C:N ratios of the Laptev (7.9) and Kara (7.5) Seas were high, and had larger contributions of terrigenous material. The highest C:N ratios were in the North Water (8.7) and Northeast Water (8.0) polynyas, and these regions were more similar to the Sterner ratio. The C:N ratio varied between regions, and was significantly different between the Atlantic (6.7) and Arctic (7.9) influenced regions of the Barents Sea, while the Atlantic dominated regions (Norwegian, Greenland and Atlantic Barents Seas) were similar (6.7–7). All observations combined, and most individual regions, showed a pattern of decreasing C:N ratios with increasing seston concentrations. This meta-analysis has important implications for ecosystem modelling, as it demonstrated the striking temporal and spatial variability in C:N ratios and challenges the common assumption of a constant C:N ratio. The non-constant stoichiometry was believed to be caused by variable contributions of autotrophs, heterotrophs and detritus to seston, and a significant decrease in C:N ratios with increasing Chlorophyll a concentrations supports this view. This study adds support to the use of a power function model, where the exponent is system-specific, but we suggest a general Arctic relationship, where POC = 7.4 PON0.89

SEAWATER pH AND THE OCEANIC CARBON CYCLE

Merged with duplicate record 10026.1/2089 on 06.20.2017 by CS (TIS)The buffering of carbon dioxide in seawater and the intimate relationship between the carbonate system, air-sea gas exchange and biological productivity in the oceans is described. Characterisation of the carbonate system is enabled through the concurrent measurement of any two of the variables pH, alkalinity, TCO2 and pCO2. It is identified that to obtain the high density, high precision measurements necessary to better constrain carbon cycle models, with respect to estimating the effect of anthropogenic carbon release to the atmosphere, it will be necessary to develop in situ techniques for the measurement of pH and pCO2. The theory of pH scales and both potentiometric and spectrophotometric pH measurement is presented as well as a chronology of pH measurements at sea. The development of automated potentiometric and spectrophotometric techniques for the simultaneous, continuous, shipboard determination of seawater pH is documented and the performance of the instrumentation on a cruise to the Southern Ocean is reported. The potentiometric system was optimised for electrode response and incorporated increased temperature control and a novel flow cell to help reduce bubble effects and maintain the integrity of the liquid junction. Nevertheless, the technique illustrated very erratic potential and the data was of unacceptable quality. The spectrophotometric technique used a flow injection technique and phenol red indicator and showed a precision off 0.005 pH unit with a sampling frequency of about 25 h-1. A comparison of calculated alkalinity from the combinations pH and pCO2 and pCO, and TCO. 2 had a residual of 1.3 17.3 4equiv. kg-1 (n = 79) or about 0.32 %. The theoretical precision of the comparison calculated from the precisions of the methods used is 0.34 %. A comparison of in situ pH and that calculated from alkalinity and TCO2 showed a standard deviation of ± 0.016 with a standard error dependent on the choice of sulphate formation constant used to convert from the free to the total hydrogen ion concentration scale. Surface pH(SWS) at 25°C has been shown to vary significantly in the Southern Ocean from 7.65 in the Bransfield Strait to 7.85 in a area of intense biological activity associated with the South Polar Front (SPF). Throughout the majority of the cruise the surface waters were undersaturated with respect to carbon dioxide and the main control on pH was from the hydrography, although in areas of high chlorophyll concentrations, associated predominantly with the SPF, there existed considerable correspondence with biological activity. A previously unknown sink for atmospheric CO2 has been identified in the Bellingshausen Sea which has significant implications for our understanding of the global carbon budget. The spectrophotometric method is put forward as the method of choice for future measurements of seawater pH.Plymouth Marine Laborator

Primary production during nutrient-induced blooms at elevated CO2 concentrations

Mesocosms experiments (PeECE II and PeECE III) were carried out in 9 transparent mesocosms. Prior to the experimental period, the seawater carbonate system was manipulated to achieve three different levels of CO2. At the onset of the experimental period, nutrients were added to all mesocosms in order to initiate phytoplankton blooms. Rates of primary production were measured by in-situ incubations using 14C-incorporation and oxygen production/consumption. Particulate primary production by 14C was also size fractionated and compared with phytoplankton species composition. Nutrient supply increased the primary production rates, and a net autotrophic phase with 14C-fixation rates up to 4 times higher than initial was observed midway through the 24 days experiment before net community production returned to near-zero and 14C-fixation rates relaxed back to lower than initial. We found a trend in the 14C-based measurements towards higher cumulative primary production at higher pCO2, consistent with recently published results for DIC removal (Riebesell et al., 2007). There where found differences to the size fractionated primary production response to CO2 treatments. The plankton composition changes throughout the bloom, however, resulted in no significant response until the final phase of the experiment where phytoplankton growth became nutrient limited, and phytoplankton community changed from diatom to flagellate dominance. This opens for the two alternative hypotheses that such an effect is associated with mineral nutrient limited growth, and/or with phytoplankton species composition. The lack of a clear net heterotrophic phase in the last part of the experiment supports the idea that a substantial part of production in the upper layer was not degraded locally, but either accumulated there or was exported vertically

A method to estimate trends in distributions of 1 min rain rates from numerical weather prediction data

It is known that the rain rate exceeded 0.01% of the time in the UK has experienced an increasing trend over the last 20 years. It is very likely that rain fade and outage experience a similar trend. This paper presents a globally applicable method to estimate these trends, based on the widely accepted Salonen-Poiares Baptista model. The input data are parameters easily extracted from numerical weather prediction reanalysis data. The method is verified using rain gauge data from the UK, and the predicted trend slopes of 0.01% exceeded rain rate are presented on a global grid