Sea ice induced changes in ocean circulation during the Eemian

We argue that Arctic sea ice played an important role during early stages of the last glacial inception. Two simulations of the Institut Pierre Simon Laplace coupled model 4 are analyzed, one for the time of maximum high latitude summer insolation during the last interglacial, the Eemian, and a second one for the subsequent summer insolation minimum, at the last glacial inception. During the inception, increased Arctic freshwater export by sea ice shuts down Labrador Sea convection and weakens overturning circulation and oceanic heat transport by 27 and 15%, respectively. A positive feedback of the Atlantic subpolar gyre enhances the initial freshening by sea ice. The reorganization of the subpolar surface circulation, however, makes the Atlantic inflow more saline and thereby maintains deep convection in the Nordic Seas. These results highlight the importance of an accurate representation of dynamic sea ice for the study of past and future climate changes.     

Effects of land cover change on the tropical circulation in a GCM

Multivariate statistics are used to investigate sensitivity of the tropical atmospheric circulation to scenario-based global land cover change (LCC), with the largest changes occurring in the tropics. Three simulations performed with the fully coupled Parallel Climate Model (PCM) are compared: (1) a present day control run; (2) a simulation with present day land cover and Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) A2 greenhouse gas (GHG) projections; and (3) a simulation with SRES A2 land cover and GHG projections. Dimensionality of PCM data is reduced by projection onto a priori specified eigenvectors, consisting of Rossby and Kelvin waves produced by a linearized, reduced gravity model of the tropical circulation. A Hotelling T ² test is performed on projection amplitudes. Effects of LCC evaluated by this method are limited to diabatic heating. A statistically significant and recurrent signal is detected for 33% of all tests performed for various combinations of parameters. Taking into account uncertainties and limitations of the present methodology, this signal can be interpreted as a Rossby wave response to prescribed LCC. The Rossby waves are shallow, large-scale motions, trapped at the equator and most pronounced in boreal summer. Differences in mass and flow fields indicate a shift of the tropical Walker circulation patterns with an anomalous subsidence over tropical South America.     

A reconstructed dynamic Indian monsoon index extended back to 1880

The authors present a reconstruction of summer (June–July–August) mean dynamic Indian monsoon index (DIMI) back to 1880 based on a large number of historical surface observation data as well as information from the upper air data. The reconstruction shows a satisfying skill in terms of both the value of reduction of error and an evaluation against other independent monsoon indices. The skill of reconstruction increases over time with more predictor data (in particular upper-level data) becoming available. A comparison with the observed all Indian summer monsoon rainfall index (AIRI) shows a high consistence in both inter-decadal and inter-annual variability. The reconstruction shows stronger than normal monsoon during the 1880s, 1915–1925 (around 1920) and 1930–1945 (around 1940) as the AIRI. The El Nino/Southern Oscillation (ENSO)—monsoon relationship is reasonably captured in the reconstruction. Powers concentrating within quasi-biennial band stand out in the reconstruction as well as in the AIRI. A comparison of the reconstruction against an atmospheric general circulation model simulation with specified SST and external forcing agents spanning 1901–1999 indicates a slightly higher reproducibility of monsoon circulation than monsoon rainfall in terms of interannual variability. The relationship between the Asian continent warming and the ENSO–monsoon connection is also discussed by using the new dynamic index.     

Evaluation of atmospheric thermal radiation algorithms for daylight hours

Existing simple but theoretically based clear-sky models for longwave down-welling radiation (LDR_c) and cloud impact algorithms transforming them to all-sky radiation (LDR) are checked against locally calibrated empirical algorithms. They are evaluated for daylight hours based on measurements in regionally differing climates of Germany. The Prata clear-sky scheme is additionally tested with adjusted coefficients so that LDR_c converges against a realistic emissivity for a completely dry atmosphere. This version is characterised by an improved modelled variance. Compared with locally calibrated schemes, bias and root mean square error (RMSE) of the more theoretical clear-sky schemes do not differ significantly and yield even better results at a mountain site. In contrast, the locally calibrated algorithms yield biases up to 9% and an increase in RMSE between 6% and 67%, if applied for other sites. For daylight hours, the cloud impact on LDR can be calculated via the ratio of observed to clear-sky global irradiation (CMF_sol). With CMF_sol, the Crawford and Duchon scheme reveals the lowest bias and a decrease in RMSE by 22% against the next best performing algorithms. Compared with synoptic cloud observations as input, the bias is reduced by 9 to 28 W m^?2 and the scattering of the residuals decreases by 20% to 30%. Based on published results for also non-European sites, it is inferred that the more theoretically based LDR_c schemes and cloud impact evaluated via CMF_sol are universally applicable and perform at least in the order of magnitude of locally calibrated empirical algorithms.     

The Budget of Turbulent Kinetic Energy in the Urban Roughness Sublayer

Full-scale observations from two urban sites in Basel, Switzerland were analysed to identify the magnitude of different processes that create, relocate, and dissipate turbulent kinetic energy (TKE) in the urban atmosphere. Two towers equipped with a profile of six ultrasonic anemometers each sampled the flow in the urban roughness sublayer, i.e. from street canyon base up to roughly 2.5 times the mean building height. This observational study suggests a conceptual division of the urban roughness sublayer into three layers: (1) the layer above the highest roofs, where local buoyancy production and local shear production of TKE are counterbalanced by local viscous dissipation rate and scaled turbulence statistics are close to to surface-layer values; (2) the layer around mean building height with a distinct inflexional mean wind profile, a strong shear and wake production of TKE, a more efficient turbulent exchange of momentum, and a notable export of TKE by transport processes; (3) the lower street canyon with imported TKE by transport processes and negligible local production. Averaged integral velocity variances vary significantly with height in the urban roughness sublayer and reflect the driving processes that create or relocate TKE at a particular height. The observed profiles of the terms of the TKE budget and the velocity variances show many similarities to observations within and above vegetation canopies.     

Thermal bioclimate in Strasbourg - the 2003 heat wave

This case study highlights the implications of the 2003 heat wave for the city of Strasbourg, France. The urban centers of France and other European countries were particularly affected by the heat wave. In some urban areas, the mortality rate was 60% above the expected value (Institute de Veille Sanitaire, 2003). The 2003 heat wave demonstrated once again that populations in urban centers are much more affected by extreme meteorological events than people living in rural areas. The aim of this analysis is to explore differences in thermal comfort conditions of (a) the city center of Strasbourg, and (b) its hinterland. The differences in thermal conditions existing between rural and urban areas are quantified by using a bio-climatological index termed physiologically equivalent temperature (PET). This index is based on the human energy balance and builds a relevant index for the quantification of the thermal environment of humans. We calculate the PET for the years 2003 and 2004 to highlight the temporal changes in the severity of climate extremes. The spatial scope of this study is improved compared to previous works in the field through the inclusion of PET calculations for five different sites on a central place in Strasbourg (Place Kléber). The calculations are characterized by different sky view factors and are compared to the reference site, which is located in a rural area. In the rural hinterland (Entzheim), the analysis of PET indicates a strong cold thermal stress during the winter months but no significant stress in summer. In 2003, summer temperatures were sensed as warmer compared to other years, but did not reach the extreme temperatures that may cause severe heat stress. For both the rural and the urban study sites PET was higher in the summer of 2003 than in 2004, which reflects the inferior thermal conditions in the urban area during the heat wave in 2003. For the entire study period, urban and rural day-time PET reached similar maximal values. Strong differences in PET, however, were observed between the rural and urban areas at night-time. The study of PET for several study sites on a central place in the city (Place Kléber) of Strasbourg for the years 2003 and 2004 showed that the sites with a higher sky view factor present higher values than sites with a lower sky view factor. The comparison of these PET values (Place Kléber) to the results for the rural area showed that during the day and the night the rural city of Entzheim has the lowest PET. During the day, the site at Place Kléber, which is located under a tree, has the lowest PET. The comparison of PET for the years 2003 and 2004 shows that PET in 2003 was about 5 to 7 K higher.     

The study of artificial intelligence for predicting land use changes in an arid ecosystem

Journal of Geographical Sciences - During the 21st century, artificial intelligence methods have been broadly applied in geosciences to simulate complex dynamic ecosystems, but the use of...     

Identifying Nonstationarity in Turbulence Series

Because of rapid forcing by varying cloud and sky conditions, turbulence time series collected in the atmospheric surface layer over land may often be nonstationary. The meteorological community, however, has no consensus definition of what nonstationarity is and, thus, no consensus method for how to identify it. This study, therefore, adopts definitions for first-order and second-order stationarity taken from the time series analysis literature and implements new analysis techniques and probabilistic tests to quantify first-order and second-order nonstationarity. First-order nonstationarity manifests as a change in the series mean; second-order nonstationarity, as a change in the variance. The analysis identifies nonstationarity in surface-level turbulent temperature and water vapour series collected during two sample days with solar forcing influenced by cirrus and cirrostratus clouds, but that nonstationarity is not as severe as expected despite the rapid thermal forcing by these clouds. On the other hand, even with negligible cloud forcing, both sample days exhibited severe nonstationarity at night.     

Uncertainty, learning and ambiguity in economic models on climate policy: some classical results and new directions

We present how uncertainty and learning are classically studied in economic models. Specifically, we study a standard expected utility model with two sequential decisions, and consider two particular cases of this model to illustrate how uncertainty and learning may affect climate policy. While uncertainty has generally a negative effect on welfare, learning has always a positive, and thus opposite, effect. The effects of both uncertainty and learning on decisions are less clear. Neither uncertainty nor learning can be used as a general argument to increase or reduce emissions today without studying the specific intertemporal costs and benefits. Considering limits in applying the expected utility framework to climate change problems, we then consider a more recent framework with ambiguity-aversion which accounts for situations of imprecise or multiple probability distributions. We discuss both the impact of ambiguity-aversion on decisions and difficulties in applying such a non-expected utility framework to a dynamic context.     

Observing upper troposphere–lower stratosphere climate with radio occultation data from the CHAMP satellite

High quality observations of the atmosphere are particularly required for monitoring global climate change. Radio occultation (RO) data, using Global Navigation Satellite System (GNSS) signals, are well suited for this challenge. The special climate utility of RO data arises from their long-term stability due to their self-calibrated nature. The German research satellite CHAllenging Minisatellite Payload for geoscientific research (CHAMP) continuously records RO profiles since August 2001 providing the first opportunity to create RO based climatologies for a multi-year period of more than 5 years. A period of missing CHAMP data from July 3, 2006 to August 8, 2006 can be bridged with RO data from the GRACE satellite (Gravity Recovery and Climate Experiment). We have built seasonal and zonal mean climatologies of atmospheric (dry) temperature, microwave refractivity, geopotential height and pressure with 10° latitudinal resolution. We show representative results with focus on dry temperatures and compare them with analysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Although we have available only about 150 CHAMP profiles per day (compared to millions of data entering the ECMWF analyses) the overall agreement between 8 and 30 km altitude is in general very good with systematic differences <0.5 K in most parts of the domain. Pronounced systematic differences (exceeding 2 K) in the tropical tropopause region and above Antarctica in southern winter can almost entirely be attributed to errors in the ECMWF analyses. Errors resulting from uneven sampling in space and time are a potential error source for single-satellite climatologies. The average CHAMP sampling error for seasonal zonal means is <0.2 K, higher values occur in restricted regions and time intervals which can be clearly identified by the sampling error estimation approach we introduced (which is based on ECMWF analysis fields). The total error of this new type of temperature climatologies is estimated to be <0.5 K below 30 km. The recently launched Taiwan/U.S. FORMOSAT-3/COSMIC constellation of 6 RO satellites started to provide thousands of RO profiles per day, but already now the single-satellite CHAMP RO climatologies improve upon modern operational climatologies in the upper troposphere–lower stratosphere and can act as absolute reference climatologies for validation of more bias-sensitive climate datasets and models.