Atmospheric circulation in regional climate models over Central Europe: links to surface air temperature and the influence of driving data

Abstract

The study examines simulation of atmospheric circulation, represented by circulation indices (flow direction, strength and vorticity), and links between circulation and daily surface air temperatures in regional climate models (RCMs) over Central Europe. We explore control simulations of five high-resolution RCMs from the ENSEMBLES project driven by re-analysis (ERA-40) and the same global climate model (ECHAM5 GCM) plus of one RCM (RCA) driven by different GCMs. The aims are to (1) identify errors in RCM-simulated distributions of circulation indices in individual seasons, (2) identify errors in simulated temperatures under particular circulation indices, and (3) compare performance of individual RCMs with respect to the driving data. Although most of the RCMs qualitatively reflect observed distributions of the airflow indices, each produces distributions significantly different from the observations. General biases include overestimation of the frequency of strong flow days and of strong cyclonic vorticity. Some circulation biases obviously propagate from the driving data. ECHAM5 and all simulations driven by ECHAM5 underestimate frequency of easterly flow, mainly in summer. Except for HIRHAM, however, all RCMs driven by ECHAM5 improve on the driving GCM in simulating atmospheric circulation. The influence on circulation characteristics in the nested RCM differs between GCMs, as demonstrated in a set of RCA simulations with different driving data. The driving data control on circulation in RCA is particularly weak for the BCM GCM, in which case RCA substantially modifies (but does not improve) the circulation from the driving data in both winter and summer. Those RCMs with the most distorted atmospheric circulation are HIRHAM driven by ECHAM5 and RCA driven by BCM. Relatively strong relationships between circulation indices and surface air temperatures were found in the observed data for Central Europe. The links differ by season and are usually stronger for daily maxima than minima. RCMs qualitatively reproduce these relationships. Effects of the driving model biases were found on RCMs’ performance in reproducing not only atmospheric circulation but also the links to surface temperature. However, the RCM formulation appears to be more important than the driving data in representing the latter. Differences of the circulation-to-temperature links among the RCA simulations are smaller and the links tend to be more realistic compared to the driving GCMs.

Appendices

Appendices

1.1 Appendix 1: Circulation indices

The airflow indices are calculated using MSLP in the grid points shown in Fig. 1 (left).

Flow strength is the total net westerly w and southerly s flow:

$$ {\text{STR}} = \sqrt {w^{2} + s^{2} } . $$

(A1)

The westerly (zonal) component of the geostrophic surface wind is calculated as the pressure gradient between 45°N and 55°N and represents the westerly flow w:

$$ w = 0.5 \cdot (p[4] + p[5]) - 0.5 \cdot (p[12] + p[13]). $$

(A2)

The southerly (meridional) component of the geostrophic surface wind represented by the pressure gradient between 10°E and 20°E is the southerly flow s:

$$ s = 1.56 \cdot (0.25 \cdot (p[13] + 2 \cdot p[9] + p[5]) - 0.25 \cdot (p[12] + 2 \cdot p[8] + p[4])). $$

(A3)

The direction of flow DIR is calculated as

$$ {\text{DIR}} = \arctan \left( \frac{w}{s} \right) $$

(A4)

added 180° if s ≥ 0, and 360° if s < 0 or w ≥ 0.

The total shear vorticity VORT is the sum of the westerly and southerly vorticities:

$$ {\text{VORT}} = zw + zs, $$

(A5)

where zw corresponds to the difference of the westerly flow between 40°N and 50°N and of that between 50°N and 60°N

$$ \begin{aligned} zw &= 1.08 \cdot (0.5 \cdot (p[1] + p[2]) - 0.5 \cdot (p[8] + p[9])) \\&\quad- 0.94 \cdot (0.5 \cdot (p[8] + p[9]) - 0.5 \cdot (p[15] + p[16])),\end{aligned} $$

(A6)

and zs is the difference of the southerly flow between 30°E and 20°E and of that between 10°E and 0°E

$$ \begin{aligned} zs &= 1.21 \cdot (0.25 \cdot (p[14] + 2 \cdot p[10] + p[6]) - 0.25 \cdot (p[13] + 2 \cdot p[9] + p[5]) \\&\quad- 0.25 \cdot (p[12] + 2 \cdot p[8] + p[4]) + 0.25 \cdot (p[11] + 2 \cdot p[7] + p[3])). \end{aligned} $$

(A7)

The constants used in these equations reflect the different sizes of grid cells at each latitude.

1.2 Appendix 2: Confidence intervals

The 95% confidence interval in Fig. 2 is calculated using the formula described by Wilks (1995):

$$ {\text{CI}} = p \pm z_{\alpha /2} \cdot \sqrt {\frac{p(1 - p)}{n}} , $$

(B1)

where p is the sample proportion (best estimate of the binomial event probability), z _α/2 stands for the 2.5% quantile of the normal distribution, and n is the sample size.