Pacific climate variability and the possible impact on global surface CO2 flux

<p>Abstract</p> <p>Background</p> <p>Climate variability modifies both oceanic and terrestrial surface CO2 flux. Using observed/assimilated data sets, earlier studies have shown that tropical oceanic climate variability has strong impacts on the land surface temperature and soil moisture, and that there is a negative correlation between the oceanic and terrestrial CO2 fluxes. However, these data sets only cover less than the most recent 20 years and are insufficient for identifying decadal and longer periodic variabilities. To investigate possible impacts of interannual to interdecadal climate variability on CO2 flux exchange, the last 125 years of an earth system model (ESM) control run are examined.</p> <p>Results</p> <p>Global integration of the terrestrial CO2 flux anomaly shows variation much greater in amplitude and longer in periodic timescale than the oceanic flux. The terrestrial CO2 flux anomaly correlates negatively with the oceanic flux in some periods, but positively in others, as the periodic timescale is different between the two variables. To determine the spatial pattern of the variability, a series of composite analyses are performed. The results show that the oceanic CO2 flux variability peaks when the eastern tropical Pacific has a large sea surface temperature anomaly (SSTA). By contrast, the terrestrial CO2 flux variability peaks when the SSTA appears in the central tropical Pacific. The former pattern of variability resembles the ENSO-mode and the latter the ENSO-modoki¹.</p> <p>Conclusions</p> <p>Our results imply that the oceanic and terrestrial CO2 flux anomalies may correlate either positively or negatively depending on the relative phase of these two modes in the tropical Pacific.</p

Process-conditioned bias correction for seasonal forecasting: a case-study with ENSO in Peru

This work assesses the suitability of a first simple attempt for process-conditioned bias correction in the context of seasonal forecasting. To do this, we focus on the northwestern part of Peru and bias correct 1- and 4-month lead seasonal predictions of boreal winter (DJF) precipitation from the ECMWF System4 forecasting system for the period 1981–2010. In order to include information about the underlying large-scale circulation which may help to discriminate between precipitation affected by different processes, we introduce here an empirical quantile–quantile mapping method which runs conditioned on the state of the Southern Oscillation Index (SOI), which is accurately predicted by System4 and is known to affect the local climate. Beyond the reduction of model biases, our results show that the SOI-conditioned method yields better ROC skill scores and reliability than the raw model output over the entire region of study, whereas the standard unconditioned implementation provides no added value for any of these metrics. This suggests that conditioning the bias correction on simple but well-simulated large-scale processes relevant to the local climate may be a suitable approach for seasonal forecasting. Yet, further research on the suitability of the application of similar approaches to the one considered here for other regions, seasons and/or variables is needed.This work has received funding from the MULTI-SDM project (MINECO/FEDER, CGL2015-66583-R). The authors are grateful to SENAMHI for the observational data, which are publicly available from http://www.senamhi.gob.pe/?p=data-historica, and to the European Center for Medium-Range Weather Forecast (ECMWF), for the access to the System4 seasonal forecasting hindcast

Focusing on fast food restaurants alone underestimates the relationship between neighborhood deprivation and exposure to fast food in a large rural area

<p>Abstract</p> <p>Background</p> <p>Individuals and families are relying more on food prepared outside the home as a source for at-home and away-from-home consumption. Restricting the estimation of fast-food access to fast-food restaurants alone may underestimate potential spatial access to fast food.</p> <p>Methods</p> <p>The study used data from the 2006 Brazos Valley Food Environment Project (BVFEP) and the 2000 U.S. Census Summary File 3 for six rural counties in the Texas Brazos Valley region. BVFEP ground-truthed data included identification and geocoding of all fast-food restaurants, convenience stores, supermarkets, and grocery stores in study area and on-site assessment of the availability and variety of fast-food lunch/dinner entrées and side dishes. Network distance was calculated from the population-weighted centroid of each census block group to all retail locations that marketed fast food (<it>n </it>= 205 fast-food opportunities).</p> <p>Results</p> <p>Spatial access to fast-food opportunities (FFO) was significantly better than to traditional fast-food restaurants (FFR). The median distance to the nearest FFO was 2.7 miles, compared with 4.5 miles to the nearest FFR. Residents of high deprivation neighborhoods had better spatial access to a variety of healthier fast-food entrée and side dish options than residents of low deprivation neighborhoods.</p> <p>Conclusions</p> <p>Our analyses revealed that identifying fast-food restaurants as the sole source of fast-food entrées and side dishes underestimated neighborhood exposure to fast food, in terms of both neighborhood proximity and coverage. Potential interventions must consider all retail opportunities for fast food, and not just traditional FFR.</p