Heat balance and eddies in the Peru-Chile current system

The Peru-Chile current System (PCS) is a region of persistent biases in global climate models. It has strong coastal upwelling, alongshore boundary currents, and mesoscale eddies. These oceanic phenomena provide essential heat transport to maintain a cool oceanic surface underneath the prevalent atmospheric stratus cloud deck, through a combination of mean circulation and eddy flux. We demonstrate these behaviors in a regional, quasi-equilibrium oceanic model that adequately resolves the mesoscale eddies with climatological forcing. The key result is that the atmospheric heating is large (>50 W m^?2) over a substantial strip >500 km wide off the coast of Peru, and the balancing lateral oceanic flux is much larger than provided by the offshore Ekman flux alone. The atmospheric heating is weaker and the coastally influenced strip is narrower off Chile, but again the Ekman flux is not sufficient for heat balance. The eddy contribution to the oceanic flux is substantial. Analysis of eddy properties shows strong surface temperature fronts and associated large vorticity, especially off Peru. Cyclonic eddies moderately dominate the surface layer, and anticyclonic eddies, originating from the nearshore poleward Peru-Chile Undercurrent (PCUC), dominate the subsurface, especially off Chile. The sensitivity of the PCS heat balance to equatorial intra-seasonal oscillations is found to be small. We demonstrate that forcing the regional model with a representative, coarse-resolution global reanalysis wind product has dramatic and deleterious consequences for the oceanic circulation and climate heat balance, the eddy heat flux in particular.     

Interpreting a Pumping Test Influenced by Another Well in an Anisotropic Aquifer

In confined aquifers the influence of neighboring active wells is often neglected when interpreting a pumping test. This can, however, lead to an erroneous interpretation of the pumping test data. This article presents simple methods to evaluate the transmissivity tensor and storativity of an anisotropic confined aquifer when there is an interfering well in the neighborhood of the tested well. Two methods have been developed depending on whether the tested well or the interfering well is the first in operation. These new methods yield better estimates of the hydraulic parameters than when the influence of the interfering well is neglected. These methods have then been used on data obtained from numerical models with an interfering well and the results have been compared to an analytical method that neglects the influence of the interfering well. The methods require knowledge of the pumping rate of the interfering well and the time elapsed since the pumping started in each well. If the interfering well started pumping before the tested well, the method does not require knowledge of the aquifer piezometric level at the beginning of the test, which is often unknown in this case. As for the method without interference, at least three monitoring wells (MWs) are needed, the position of which influences the accuracy of the estimated parameters. Some recommendations concerning MWs position have been given to get more accurate results according to the sought parameter.     

Coupling statistical and dynamical methods for spatial downscaling of precipitation

The resolution of General Circulation Models (GCMs) is too coarse for climate change impact studies at the catchment or site-specific scales. To overcome this problem, both dynamical and statistical downscaling methods have been developed. Each downscaling method has its advantages and drawbacks, which have been described in great detail in the literature. This paper evaluates the improvement in statistical downscaling (SD) predictive power when using predictors from a Regional Climate Model (RCM) over a GCM for downscaling site-specific precipitation. Our approach uses mixed downscaling, combining both dynamic and statistical methods. Precipitation, a critical element of hydrology studies that is also much more difficult to downscale than temperature, is the only variable evaluated in this study. The SD method selected here uses a stepwise linear regression approach for precipitation quantity and occurrence (similar to the well-known Statistical Downscaling Model (SDSM) and called SDSM-like herein). In addition, a discriminant analysis (DA) was tested to generate precipitation occurrence, and a weather typing approach was used to derive statistical relationships based on weather types, and not only on a seasonal basis as is usually done. The existing data record was separated into a calibration and validation periods. To compare the relative efficiency of the SD approaches, relationships were derived at the same sites using the same predictors at a 300km scale (the National Center for Environmental Prediction (NCEP) reanalysis) and at a 45km scale with data from the limited-area Canadian Regional Climate Model (CRCM) driven by NCEP data at its boundaries. Predictably, using CRCM variables as predictors rather than NCEP data resulted in a much-improved explained variance for precipitation, although it was always less than 50?% overall. For precipitation occurrence, the SDSM-like model slightly overestimated the frequencies of wet and dry periods, while these were well-replicated by the DA-based model. Both the SDSM-like and DA-based models reproduced the percentage of wet days, but the wet and dry statuses for each day were poorly downscaled by both approaches. Overall, precipitation occurrence downscaled by the DA-based model was much better than that predicted by the SDSM-like model. Despite the added complexity, the weather typing approach was not better at downscaling precipitation than approaches without classification. Overall, despite significant improvements in precipitation occurrence prediction by the DA scheme, and even going to finer scales predictors, the SD approach tested here still explained less than 50?% of the total precipitation variance. While going to even smaller scale predictors (10–15?km) might improve results even more, such smaller scales would basically transform the direct outputs of climate models into impact models, thus negating the need for statistical downscaling approaches.     

The stratospheric version of LMDz: dynamical climatologies, arctic oscillation, and impact on the surface climate

A climatology of the stratosphere is determined from a 20-year integration with the stratospheric version of the Atmospheric General Circulation Model LMDz. The model has an upper boundary at near 65 km, uses a Doppler spread non-orographic gravity waves drag parameterization and a subgrid-scale orography parameterization. It also has a Rayleigh damping layer for resolved waves only (not the zonal mean flow) over the top 5 km. This paper describes the basic features of the model and some aspects of its radiative-dynamical climatology. Standard first order diagnostics are presented but some emphasis is given to the model’s ability to reproduce the low frequency variability of the stratosphere in the winter northern hemisphere. In this model, the stratospheric variability is dominated at each altitudes by patterns which have some similarities with the arctic oscillation (AO). For those patterns, the signal sometimes descends from the stratosphere to the troposphere. In an experiment where the parameterized orographic gravity waves that reach the stratosphere are exaggerated, the model stratosphere in the NH presents much less variability. Although the stratospheric variability is still dominated by patterns that resemble to the AO, the downward influence of the stratosphere along these patterns is near entirely lost. In the same time, the persistence of the surface AO decreases, which is consistent with the picture that this persistence is linked to the descent of the AO signal from the stratosphere to the troposphere. A comparison between the stratospheric version of the model, and its routinely used tropospheric version is also done. It shows that the introduction of the stratosphere in a model that already has a realistic AO persistence can lead to overestimate the actual influence of the stratospheric dynamics onto the surface AO. Although this result is certainly model dependent, it suggests that the introduction of the stratosphere in a GCM also call for a new adjustment of the model parameters that affect the tropospheric variability.     

The LMDZ4 general circulation model: climate performance and sensitivity to parametrized physics with emphasis on tropical convection

The LMDZ4 general circulation model is the atmospheric component of the IPSL–CM4 coupled model which has been used to perform climate change simulations for the 4th IPCC assessment report. The main aspects of the model climatology (forced by observed sea surface temperature) are documented here, as well as the major improvements with respect to the previous versions, which mainly come form the parametrization of tropical convection. A methodology is proposed to help analyse the sensitivity of the tropical Hadley–Walker circulation to the parametrization of cumulus convection and clouds. The tropical circulation is characterized using scalar potentials associated with the horizontal wind and horizontal transport of geopotential (the Laplacian of which is proportional to the total vertical momentum in the atmospheric column). The effect of parametrized physics is analysed in a regime sorted framework using the vertical velocity at 500 hPa as a proxy for large scale vertical motion. Compared to Tiedtke’s convection scheme, used in previous versions, the Emanuel’s scheme improves the representation of the Hadley–Walker circulation, with a relatively stronger and deeper large scale vertical ascent over tropical continents, and suppresses the marked patterns of concentrated rainfall over oceans. Thanks to the regime sorted analyses, these differences are attributed to intrinsic differences in the vertical distribution of convective heating, and to the lack of self-inhibition by precipitating downdraughts in Tiedtke’s parametrization. Both the convection and cloud schemes are shown to control the relative importance of large scale convection over land and ocean, an important point for the behaviour of the coupled model.     

Multispectral remote sensing of biomass burning in West Africa

Remote sensing measurements provide a vauable means of determining the extent of burning areas and of estimating the overall distribution of pollutant sources (identified from experimental studies) in time and space. This distribution has to be taken into account in the boundary conditions of chemistry atmospheric models.Recent methods developed for the remote sensing of active fires in tropical or temperated forest zones, have been found to be completely inadequate for fire detection on West African savannas. In order to accurately estimate the active fire distribution in the function of different sorts of West African savannas (Sahelian, Sudanian and Guinean) and forests, a multispectral methodology has been developed based on NOAA/11-AVHRR satellite data, with the purpose of eliminating as much as possible the problems related to large surface heterogeneity, confusion and bias, produced by clouds, smoke, haze, background emissivities, etc.Unlike other methods, the results show that the multispectral method, in spite of its selectivity, provides realistic results, and does not under- or over-estimate the number of fires that can be sensed by the satellite. Consequently, this methodology is more appropriate than the simplest ones for a systematic sensing of this phenomenon.     

Preserving the Ocean Circulation: Implications for Climate Policy

Climate modelers have recognized the possibility of abrupt climate changes caused by a reorganization of the North Atlantic's current pattern (technically known as a thermohaline circulation collapse). This circulation system now warms north-western Europe and transports carbon dioxide to the deep oceans. The posited collapse of this system could produce severe cooling in northwestern Europe, even when general global warming is in progress. In this paper we use a simple integrated assessment model to investigate the optimal policy response to this risk. Adding the constraint of avoiding a thermohaline circulation collapse would significantly reduce the allowable greenhouse gas emissions in the long run along an optimal path. Our analysis implies that relatively small damages associated with a collapse (less than 1% of gross world product) would justify a considerable reduction of future carbon dioxide emissions.     

Water-poverty relationships in the coastal town of Mbour (Senegal): Relevance of GIS for decision support

Coastal area is always a zone with complex problems. Due to the attraction they exert, are facing many social problems. Therefore, a coastal city is usually a city with problems. Its extension, caused by the influx of people from different backgrounds, creates an increased demand for services. One of the problems frequently encountered, especially in Senegal, is access to water. The problem of access to water is poorly treated, without being correlated with the urban evolution, i.e. with increasing population and demand growth. The water resource is facing numerous complications such as the lack of integrated management, integration issues at the governance level, where the local factor is often forgotten.The town of Mbour, object of our study, does not come out of that lot, being an attractive coastal city, from an African country. This indicates the need for an integrated management oriented from local to a global basis and not vice versa. The study presented in this paper indicates that a large proportion of the population has not access to a verified drinking water system and uses water from wells or standpipes. Half of the surveyed population (50%) has no access to a water supply system. The water poverty map of the town overlaps with that of the general poverty excepting few neighborhoods. This means that even areas that are not affected by poverty have a very low or poor access to water, which so far remains the perverse effect of the reform of the Senegalese water sector in 1995.     

Assessing regression‐based statistical approaches for downscaling precipitation over North America

This paper assesses linear regression‐based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45‐ and 15‐km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger‐scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher‐resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation‐generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression‐based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd.