Biases in the diurnal temperature range in Central Europe in an ensemble of regional climate models and their possible causes

The study examines how regional climate models (RCMs) reproduce the diurnal temperature range (DTR) in their control simulations over Central Europe. We evaluate 30-year runs driven by perfect boundary conditions (the ERA40 reanalysis, 1961–1990) and a global climate model (ECHAM5) of an ensemble of RCMs with 25-km resolution from the ENSEMBLES project. The RCMs’ performance is compared against the dataset gridded from a high-density stations network. We find that all RCMs underestimate DTR in all seasons, notwithstanding whether driven by ERA40 or ECHAM5. Underestimation is largest in summer and smallest in winter in most RCMs. The relationship of the models’ errors to indices of atmospheric circulation and cloud cover is discussed to reveal possible causes of the biases. In all seasons and all simulations driven by ERA40 and ECHAM5, underestimation of DTR is larger under anticyclonic circulation and becomes smaller or negligible for cyclonic circulation. In summer and transition seasons, underestimation tends to be largest for the southeast to south flow associated with warm advection, while in winter it does not depend on flow direction. We show that the biases in DTR, which seem common to all examined RCMs, are also related to cloud cover simulation. However, there is no general tendency to overestimate total cloud amount under anticyclonic conditions in the RCMs, which suggests the large negative bias in DTR for anticyclonic circulation cannot be explained by a bias in cloudiness. Errors in simulating heat and moisture fluxes between land surface and atmosphere probably contribute to the biases in DTR as well.     

Turbulent Kinetic Energy Dissipation in the Surface Layer

We estimated the turbulent kinetic energy (TKE) dissipation rate for thirty-two 1-h intervals of unstable stratification covering the stability range 0.12 ≤ −z/L ≤ 43 (z/L is the ratio of instrument height to the Obukhov length), by fitting Kolmogorov’s inertial subrange spectrum to streamwise spectra observed over a desert flat. Estimated values are compatible with the existence of local equilibrium, in that the TKE dissipation rate approximately equalled the sum of shear and buoyant production rates. Only in the neutral limit was the turbulent transport term in the TKE budget measured to be small.     

Development of satellite green vegetation fraction time series for use in mesoscale modeling: application to the European heat wave 2006

A method is presented for development of satellite green vegetation fraction (GVF) time series for use in the Weather Research and Forecasting (WRF) model. The GVF data is in the WRF model used to describe the temporal evolution of many land surface parameters, in addition to the evolution of vegetation. Several high-resolution GVF products, derived from high-quality satellite retrievals from Moderate Resolution Imaging Spectroradiometer images, were produced and their performance was evaluated in long-term WRF simulations. The atmospheric conditions during the 2006 heat wave year over Europe were simulated since significant interannual variability in vegetation seasonality was found. Such interannual variability is expected to increase in the coming decades due to climatic changes. The simulation using a quadratic normalized difference vegetation index to GVF relationship resulted in consistent improvements of modeled temperatures. The model mean temperature cold bias was reduced by 10 % for the whole domain and by 20–45 % in areas affected by the heat wave. The study shows that WRF simulations during heat waves and droughts, when vegetation conditions deviate from the climatology, require concurrent land surface properties in order to produce accurate results.     

URBAN CHANGE,CIRCUITS OF CAPITAL,AND UNEVEN DEVELOPMENT*

The circuits of capital/uneven development model is widely used in geography, urban planning, and the social sciences. Its principal weakness is its marginalizing of local influences in explaining the intensity and patterning of metropolitan investment. A deeper consideration of local culture, politics, and biographies is needed to sensitize the model to the particularities of place. Local creative actors set in motion place-specific forms of change whose logic is tied to structured circumstances.     

The reversal of the solar polar magnetic fields

Observations of the first major active regions and large-scale magnetic field patterns of Cycle 22 are presented. These show that, following the emergence of a trans-equatorial pattern, or cell, of positive flux related to old cycle activity, the first new cycle active regions of the longitude range emerged across the neutral lines of this cell, which continued to grow and expand across the equator for several rotations. The development of a parallel trans-equatorial band of flux of opposite (negative) polarity and the emergence of both new and old cycle active regions across a neutral line of this cell are also described.Simulations using the flux transport equation, and based on synoptic magnetic data provided by the Mount Wilson Observatory, show that, while the growth of the positive region could, in part, be explained by the decay of flux from these new regions, there were significant differences between synoptic contour charts based on the simulations and those constructed from the observed fields. They also show that the development of the negative region cannot reasonably be explained by the decay of the observed active regions.A further example of the counter rotation of decaying active region fields is reported. Here the initial tilt of the negative-positive magnetic axes of two adjacent regions is normal, and simulations based on these data show their combined follower flux moving preferentially polewards. However, the observations show that, after three rotations, the decaying leader flux is entirely poleward of the follower flux.On leave from the School of Mathematics, University of Sydney.     

Moment method for multiple scattering of solar Lα radiation in the nearby interstellar medium

The problem of solar L (1216 Å) photons scattered coherently by interplanetary medium is solved for a realistic density distribution using a simple three-stream division of the radiation field.     

On the average rate of growth in sunspot number and the size of the sunspot cycle

The average rate of growth during the ascending portion of the sunspot cycle, defined here as the difference in smoothed sunspot number values between elapsed time (in months) t and sunspot minimum divided by t, is shown to correlate (r 0.78) with the size of the sunspot cycle, especially for t 18 months. Also, the maximum value of the average rate of growth is shown to highly correlate (r = 0.98) with the size of the cycle. Based on the first 18 months of the cycle, cycle 22 is projected to have an R(M) = 186.0 ± 27.2 (at the ± 1 level), and based on the first 24 months of the cycle, it is projected to have an R(M) = 201.0 ± 20.1 (at the ± 1 level). Presently, the average rate of growth is continuing to rise, having a value of about 4.5 at 24 months into the cycle, a value second only to that of cycle 19 (4.8 at t = 24 and a maximum value of 5.26 at t = 27). Using 4.5 as the maximum value of the average rate of growth for cycle 22, a lower limit can be estimated for R(M); namely R(M) for cycle 22 is estimated to be 164.0 (at the 97.5% level of confidence). Thus, these findings are consistent with the previous single variate predictions that project R(M) for cycle 22 to be one of the greatest on record, probably larger than cycle 21 (164.5) and near that of cycle 19 (201.3).     

On the level of skill in predicting maximum sunspot number: A comparative study of single variate and bivariate precursor techniques

Precursor prediction techniques have generally performed well in predicting the maximum amplitude of sunspot cycles, based on cycles 10–21. Single variate methods based on minimum sunspot amplitude have reliably predicted the size of the sunspot cycle 9 out of 12 times, where a reliable prediction is defined as one having an observed maximum amplitude within the prediction interval (determined from the average error). On the other hand, single variate methods based on the size of the geomagnetic minimum have reliably predicted the size of the sunspot cycle 8 of 10 times (geomagnetic data are only available since about cycle 12). Bivariate prediction methods have, thus far, performed flawlessly, giving reliable predictions 10 out of 10 times (bivariate methods are based on sunspot and geomagnetic data). For cycle 22, single variate methods (based on geomagnetic data) suggest a maximum amplitude of about 170 ± 25, while bivariate methods suggest a maximum amplitude of about 140 ± 15; thus, both techniques suggest that cycle 22 will be of smaller maximum amplitude than that observed during cycle 19, and possibly even smaller than that observed for cycle 21. Compared to the mean cycle, cycle 22 is presently behaving as if it is a + 2.6 cycle (maximum amplitude about 225). It appears then that either cycle 22 will be the first cycle not to be reliably predicted by the combined precursor techniques (i.e., cycle 22 is an anomaly, a statistical outlier) or the deviation of cycle 22 relative to the mean cycle will substantially decrease over the next 18 months. Because cycle 22 is a large amplitude cycle, maximum smoothed sunspot number is expected to occur early in 1990 (between December 1989 and May 1990).     

On the maximum rate of change in sunspot number growth and the size of the sunspot cycle

Statistically significant correlations exist between the size (maximum amplitude) of the sunspot cycle and, especially, the maximum value of the rate of rise during the ascending portion of the sunspot cycle, where the rate of rise is computed either as the difference in the month-to-month smoothed sunspot number values or as the average rate of growth in smoothed sunspot number from sunspot minimum. Based on the observed values of these quantities (equal to 10.6 and 4.63, respectively) as of early 1989, one infers that cycle 22's maximum amplitude will be about 175 ± 30 or 185 ± 10, respectively, where the error bars represent approximately twice the average error found during cycles 10–21 from the two fits.