Ocean heat content variability and change in an ensemble of ocean reanalyses

Accurate knowledge of the location and magnitude of ocean heat content (OHC) variability and change is essential for understanding the processes that govern decadal variations in surface temperature, quantifying changes in the planetary energy budget, and developing constraints on the transient climate response to external forcings. We present an overview of the temporal and spatial characteristics of OHC variability and change as represented by an ensemble of dynamical and statistical ocean reanalyses (ORAs). Spatial maps of the 0–300 m layer show large regions of the Pacific and Indian Oceans where the interannual variability of the ensemble mean exceeds ensemble spread, indicating that OHC variations are well-constrained by the available observations over the period 1993–2009. At deeper levels, the ORAs are less well-constrained by observations with the largest differences across the ensemble mostly associated with areas of high eddy kinetic energy, such as the Southern Ocean and boundary current regions. Spatial patterns of OHC change for the period 1997–2009 show good agreement in the upper 300 m and are characterized by a strong dipole pattern in the Pacific Ocean. There is less agreement in the patterns of change at deeper levels, potentially linked to differences in the representation of ocean dynamics, such as water mass formation processes. However, the Atlantic and Southern Oceans are regions in which many ORAs show widespread warming below 700 m over the period 1997–2009. Annual time series of global and hemispheric OHC change for 0–700 m show the largest spread for the data sparse Southern Hemisphere and a number of ORAs seem to be subject to large initialization ‘shock’ over the first few years. In agreement with previous studies, a number of ORAs exhibit enhanced ocean heat uptake below 300 and 700 m during the mid-1990s or early 2000s. The ORA ensemble mean (±1 standard deviation) of rolling 5-year trends in full-depth OHC shows a relatively steady heat uptake of approximately 0.9 ± 0.8 W m−2 (expressed relative to Earth’s surface area) between 1995 and 2002, which reduces to about 0.2 ± 0.6 W m−2 between 2004 and 2006, in qualitative agreement with recent analysis of Earth’s energy imbalance. There is a marked reduction in the ensemble spread of OHC trends below 300 m as the Argo profiling float observations become available in the early 2000s. In general, we suggest that ORAs should be treated with caution when employed to understand past ocean warming trends—especially when considering the deeper ocean where there is little in the way of observational constraints. The current work emphasizes the need to better observe the deep ocean, both for providing observational constraints for future ocean state estimation efforts and also to develop improved models and data assimilation methods.

Spatio-temporal variability of polynya dynamics and ice production in the Laptev Sea between the winters of 1979/80 and 2007/08

Polynyas in the Laptev Sea are examined with respect to recurrence and interannual wintertime ice production.We use a polynya classification method based on passive microwave satellite data to derive daily polynya area from long-term sea-ice concentrations. This provides insight into the spatial and temporal variability of open-water and thin-ice regions on the Laptev Sea Shelf. Using thermal infrared satellite data to derive an empirical thin-ice distribution within the thickness range from 0 to 20 cm, we calculate daily average surface heat loss and the resulting wintertime ice formation within the Laptev Sea polynyas between 1979 and 2008 using reanalysis data supplied by the National Centers for Environmental Prediction, USA, as atmospheric forcing. Results indicate that previous studies significantly overestimate the contribution of polynyas to the ice production in the Laptev Sea. Average wintertime ice production in polynyas amounts to approximately 55 km39 27% and is mostly determined by the polynya area, wind speed and associated large-scale circulation patterns. No trend in ice production could be detected in the period from 1979/80 to 2007/08.

A new method for the characterisation and verification of local spatial predictability for convective scale ensembles

The use of kilometre-scale ensembles in operational forecasting provides new challenges for forecast interpretation and evaluation to account for uncertainty on the convective scale. A new neighbourhood based method is presented for evaluating and characterising the local predictability variations from convective scale ensembles. Spatial scales over which ensemble forecasts agree (agreement scales, S^A) are calculated at each grid point ij, providing a map of the spatial agreement between forecasts. By comparing the average agreement scale obtained from ensemble member pairs (S^A(mm)_ij), with that between members and radar observations (S^A(mo)_ij), this approach allows the location-dependent spatial spread-skill relationship of the ensemble to be assessed. The properties of the agreement scales are demonstrated using an idealised experiment. To demonstrate the methods in an operational context the S^A(mm)_ij and S^A(mo)_ij are calculated for six convective cases run with the Met Office UK Ensemble Prediction System. The S^A(mm)_ij highlight predictability differences between cases, which can be linked to physical processes. Maps of S^A(mm)_ij are found to summarise the spatial predictability in a compact and physically meaningful manner that is useful for forecasting and for model interpretation. Comparison of S^A(mm)_ij and S^A(mo)_ij demonstrates the case-by-case and temporal variability of the spatial spread-skill, which can again be linked to physical processes.

Exploiting satellite-based rainfall for weather index insurance: the challenges of spatial and temporal aggregation

Lack of access to insurance exacerbates the impact of climate variability on smallholder famers in Africa. Unlike traditional insurance, which compensates proven agricultural losses, weather index insurance (WII) pays out in the event that a weather index is breached. In principle, WII could be provided to farmers throughout Africa. There are two data-related hurdles to this. First, most farmers do not live close enough to a rain gauge with sufficiently long record of observations. Second, mismatches between weather indices and yield may expose farmers to uncompensated losses, and insurers to unfair payouts – a phenomenon known as basis risk. In essence, basis risk results from complexities in the progression from meteorological drought (rainfall deficit) to agricultural drought (low soil moisture). In this study, we use a land-surface model to describe the transition from meteorological to agricultural drought. We demonstrate that spatial and temporal aggregation of rainfall results in a clearer link with soil moisture, and hence a reduction in basis risk. We then use an advanced statistical method to show how optimal aggregation of satellite-based rainfall estimates can reduce basis risk, enabling remotely sensed data to be utilized robustly for WII.

A Spatial-Temporal Downscaling Approach To Construction Of Rainfall Intensity-Duration-Frequency Relations In The Context Of Climate Change

This paper proposes a spatial-temporal downscaling approach to construction of the intensity-duration-frequency (IDF) relations at a local site in the context of climate change and variability. More specifically, the proposed approach is based on a combination of a spatial downscaling method to link large-scale climate variables given by General Circulation Model (GCM) simulations with daily extreme precipitations at a site and a temporal downscaling procedure to describe the relationships between daily and sub-daily extreme precipitations based on the scaling General Extreme Value (GEV) distribution. The feasibility and accuracy of the suggested method were assessed using rainfall data available at eight stations in Quebec (Canada) for the 1961-2000 period and climate simulations under four different climate change scenarios provided by the Canadian (CGCM3) and UK (HadCM3) GCM models. Results of this application have indicated that it is feasible to link sub-daily extreme rainfalls at a local site with large-scale GCM-based daily climate predictors for the construction of the IDF relations for present (1961-1990) and future (2020s, 2050s, and 2080s) periods at a given site under different climate change scenarios. In addition, it was found that annual maximum rainfalls downscaled from the HadCM3 displayed a smaller change in the future, while those values estimated from the CGCM3 indicated a large increasing trend for future periods. This result has demonstrated the presence of high uncertainty in climate simulations provided by different GCMs. In summary, the proposed spatial-temporal downscaling method provided an essential tool for the estimation of extreme rainfalls that are required for various climate-related impact assessment studies for a given region.

Estimating abundance and spatial distribution patterns of the bubble crab Dotilla fenestrata (Crustacea : Brachyura)

The bubble crab Dotilla fenestrata forms very dense populations on the sand flats of the eastern coast of Inhaca Island, Mozambique, making it an interesting biological model to examine spatial distribution patterns and test the relative efficiency of common sampling methods. Due to its apparent ecological importance within the sandy intertidal community, understanding the factors ruling the dynamics of Dotilla populations is also a key issue. In this study, different techniques of estimating crab density are described, and the trends of spatial distribution of the different population categories are shown. The studied populations are arranged in discrete patches located at the well-drained crests of nearly parallel mega sand ripples. For a given sample size, there was an obvious gain in precision by using a stratified random sampling technique, considering discrete patches as strata, compared to the simple random design. Density average and variance differed considerably among patches since juveniles and ovigerous females were found clumped, with higher densities at the lower and upper shore levels, respectively. Burrow counting was found to be an adequate method for large-scale sampling, although consistently underestimating actual crab density by nearly half. Regression analyses suggested that crabs smaller than 2.9 mm carapace width tend to be undetected in visual burrow counts. A visual survey of sampling plots over several patches of a large Dotilla population showed that crab density varied in an interesting oscillating pattern, apparently following the topography of the sand flat. Patches extending to the lower shore contained higher densities than those mostly covering the higher shore. Within-patch density variability also pointed to the same trend, but the density increment towards the lowest shore level varied greatly among the patches compared.

The sugarcane borer Diatraea saccharalis (Fabr.) (Lep., Crambidae) and its parasitoids: a synchrony approach to spatial and temporal dynamics

A data set on Diatraea saccharalis and its parasitoids, Cotesia flavipes and tachinid flies, was analysed at five spatial scales-sugarcane mill, region, intermediary, farm and zone-to determine the role of spatial scale in synchrony patterns, and on temporal population variability. To analyse synchrony patterns, only the three highest spatial scales were considered, but for temporal population variability, all spatial scales were adopted. The synchrony-distance relationship revealed complex spatial structures depending on both species and spatial scale. Temporal population variability [SD log(x+1)] levels were highest at the smallest spatial scales although, in the majority of the cases, temporal variability was inversely dependent on sample size. All the species studied, with a few exceptions, presented spatial synchrony independent of spatial scale. The tachinid flies exhibited stronger synchrony dynamics than D. saccharalis and C. flavipes in all spatial scales with the latter displaying the weakest synchrony levels, except when mill spatial scales were compared. In some cases spatial synchrony may at first decay and then increase with distance, but the presence of such patterns can change depending on the spatial scale adopted.

Short-term variability of copepod abundance in Jurumirim Reservoir, São Paulo, Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Spatial and linear correlations between soil and corn

O conjunto de tecnologias aplicadas ao sistema de produção agrícola tem como características principais a verticalização da produtividade, diminuição de custos, melhoria nas características físicas, químicas e biológicas do solo para proporcionarem o crescimento sustentável do meio de produção. Desta forma, o trabalho teve como objetivo determinar a variabilidade e as correlações lineares e especiais entre os atributos da planta e do solo, visando selecionar um indicador da qualidade física do solo de boa representatividade para produção de forragem. No ano agrícola de 2006, na Fazenda Bonança, município de Pereira Barreto (SP), foram analisados a produtividade de forragem do milho outonal (FDM) em sistema plantio direto irrigado e os atributos físicos do solo, num Latossolo Vermelho distrófico. O objetivo foi estudar a variabilidade e as correlações lineares e espaciais entre os atributos da planta e os do solo, visando selecionar um indicador da qualidade física do solo de boa representatividade para a produtividade da forragem. Foi instalada a malha geoestatística, para coleta de dados do solo e planta, contendo 125 pontos amostrais, numa área de 2.500 m². Os atributos estudados, além de não terem variado aleatoriamente, apresentaram variabilidade dos dados entre baixa e muito alta e seguiram padrões espaciais bem definidos, com alcances entre 7,8 e 38,0 m. Por outro lado, a correlação linear entre os atributos da planta com o do solo foi baixa e extremamente significativa. Os pares Massa Seca de forragem versus Microporosidade e Diâmetro do colmo versus Densidade do Solo foram melhor correlacionados na camada de 0-0.10m, enquanto os outros pares - Massa Seca de Forragem versus Macroporosidade - e Porosidade Total - apresentaram correlação inversa para a mesma camada. Entretanto, do ponto de vista espacial, houve uma alta correlação inversa entre Massa Seca de Forragem com Microporosidade, de modo que a microporosidade na camada de 0-0.10m pode ser considerada um bom indicador de qualidade física do solo, tendo em vista a produção de forragem de milho.

Genetic variability in natural populations of Zeyheria montana mart. from the Brazilian Cerrado

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

SPATIAL AUTOCORRELATION OF MORPHOMETRIC VARIATION IN LUTOSA-BRASILIENSIS (BRUNNER VONWATTENWYL, 1888) (ORTHOPTERA, GRYLLACRIDOIDEA, HENICIDAE)

Geographic differentiation and sexual dimorphism in eighteen morphometric characters of Lutosa brasiliensis (Orthoptera: Henicidae) collected in eight localities of the State of São Paulo (Brazil) were analysed. A two-way Multivariate Analysis of Variance (MONOVA) was used to assess simultaneously the effects of sex and geographic location (plus their interaction) on morphometric variability. The spatial patterns of variation were analysed by Factor and Spatial Autocorrelation Analyses (Moran's I coefficient in four distance classes). Both indicate that the main direction of variation is, for males and females, a north-south cline in overall body size. In females, however, ovipositor length is not correlated with overall body size and displays a different pattern of variation over geographic space, indicating that distinct evolutionary forces produced the geographic differentiation in the species.

GEOGRAPHIC-VARIATION IN APIS-CERANA-INDICA F - A SPATIAL AUTOCORRELATION ANALYSIS OF MORPHOMETRIC PATTERNS

Spatial patterns of morphometric variation in Apis cerana indica were analysed. Factor and spatial autocorrelation analyses were applied to 29 characters, measured in 17 populations in India. Correlograms showed that 15 characters are patterned geographically, and 13 of them are related to overall size. These characters are distributed as a north-south cline, probably reflecting adaptations to environmental conditions. However, the great number of characteristics without geographical pattern suggests that part of the morphometric variability is due to local stochastic divergences.

Distribution of carabid beetles in agroecosystems across spatial scales - A review

The review focuses on the questions (1) how does the spatial heterogeneity of landscape influences carabid biodiversity, and (2) what are the main factors causing this biodiversity across nested spatial scales (study point - plant association - landscape level). The analysis of recent literature indicates that the spatial distribution of carabids differs at various spatial scales, and the factors responsible for the distribution are different. At the study point level most of the communities exhibit high variability of population density and diversity, which has no correlations with soil, and sometimes, vegetation, parameters. Most of the factors that contribute to formation of the communities are stochastic, simply because patches of a factor are much smaller than the size of a distinct carabid community. At the level of plant association, soil factors begin to play the role in driving the communities. At this level, litter depth, micro-climate and vegetation composition are the main factors. At the landscape level, geological factors, such as topography, landscape geochemistry, and history are playing important roles. As a conservation measure, spatial heterogeneity should be kept at all spatial scales at the same time to maintain carabid biodiversity in agricultural areas.

A Spatial and Temporal Prediction Model of Corn Grain Yield as a Function of Soil Attributes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)