Evaluation of water and energy budgets in regional climate models applied over Europe

Abstract

This study presents a model intercomparison of four regional climate models (RCMs) and one variable resolution atmospheric general circulation model (AGCM) applied over Europe with special focus on the hydrological cycle and the surface energy budget. The models simulated the 15 years from 1979 to 1993 by using quasi-observed boundary conditions derived from ECMWF re-analyses (ERA). The model intercomparison focuses on two large atchments representing two different climate conditions covering two areas of major research interest within Europe. The first is the Danube catchment which represents a continental climate dominated by advection from the surrounding land areas. It is used to analyse the common model error of a too dry and too warm simulation of the summertime climate of southeastern Europe. This summer warming and drying problem is seen in many RCMs, and to a less extent in GCMs. The second area is the Baltic Sea catchment which represents maritime climate dominated by advection from the ocean and from the Baltic Sea. This catchment is a research area of many studies within Europe and also covered by the BALTEX program. The observed data used are monthly mean surface air temperature, precipitation and river discharge. For all models, these are used to estimate mean monthly biases of all components of the hydrological cycle over land. In addition, the mean monthly deviations of the surface energy fluxes from ERA data are computed. Atmospheric moisture fluxes from ERA are compared with those of one model to provide an independent estimate of the convergence bias derived from the observed data. These help to add weight to some of the inferred estimates and explain some of the discrepancies between them. An evaluation of these biases and deviations suggests possible sources of error in each of the models. For the Danube catchment, systematic errors in the dynamics cause the prominent summer drying problem for three of the RCMs, while for the fourth RCM this is related to deficiencies in the land surface parametrization. The AGCM does not show this drying problem. For the Baltic Sea catchment, all models similarily overestimate the precipitation throughout the year except during the summer. This model deficit is probably caused by the internal model parametrizations, such as the large-scale condensation and the convection schemes.

Appendix 1

1.1 Derivation of observational estimates

First, the derivation of estimates of “observed” total runoff from the observed discharge is described. The idea behind this method is to establish a statistical relation (Eq. (5)) between model-computed monthly ensemble mean values of total runoff within a specific catchment (here the Danube and the Baltic Sea catchments) and model-computed monthly ensemble mean values of discharge from that catchment based on the HIRHAM model simulation and the hydrological discharge (HD) model (Hagemann and Dümenil 1999). Having established such a relation it is then used to obtain orresponding quasi-observed values of runoff from observed values of discharge. The HD model separates the lateral water flow into the three flow processes of overland flow, base flow, and river flow. Overland flow uses surface runoff as input, baseflow is fed by drainage from the soil and the inflow from other grid boxes contributes to riverflow. The sum of the three flow processes is equal to the total outflow from a grid box. As a general strategy, the HD model computes daily discharge at a latitude-longitude grid with 0.5° resolution. The model input fields of runoff and drainage resulting from the various (global or regional) general circulation model resolutions are therefore interpolated to the same 0.5° grid. In this study, the runoff and drainage fields of the HIRHAM simulation (see Sect. 2.1) were used to obtain the simulated discharge.

For a certain catchment, we assume that the relation between runoff R and discharge D can be approximated by Eq. (5) where L is an average lag time between R and D and the factor a is approximating a smoothing with time.

$$D(t) = a(t) \cdot R(t - L)$$

(5)

Optimum values of L and a _i were determined for each of the 12 calendar months from the 15 year time series of monthly total runoff and discharge values using a least square fit, allowing for integer lag values only. For the Danube catchment as well as for the Baltic Sea catchment, an optimum lag value L = 1 month was found. Thus, Eq. (5) becomes

$$D_i = a_i \cdot R_{i - 1} $$

(6)

for month i. The a _i for both catchments are shown in Table 7. Assuming that Eq. (6) with the model determined a _i is valid also for observed values of discharge and runoff a set of 12 quasi-observed runoff ensemble mean values, R _obs , can be estimated from the observed long term mean discharge values available. Note that the long term annual means of D and R are equal.

Table 7 Optimum values of a _i for month i obtained from Eq. (6)

Next, the estimation of the “observed” monthly storage changes ΔWS is described. Here, a similar statistical method was chosen as for the runoff. As these storage changes largely depend on the precipitation we approximate this relation by

$$\Delta WS_i = b_i \cdot P_i $$

(7)

Here, again we determine the coefficients b _i for each of the 12 calendar months from the 15 years-time series of monthly ΔWS and P of the corresponding RCM, e.g. the HIRHAM model, by a least square method. In order to obtain quasi-observed ΔWS values, ΔWS _obs , from observed P values the model determined relation between ΔWS and P is assumed to be valid also in reality. Using Eq. (7), we can then estimate the ΔWS _obs from the CRU precipitation data, P _obs . In this way, a quasi-observed evapotranspiration E _obs was estimated by inserting P _obs , R _obs and ΔWS _obs into Eq. (1). The so determined observed and quasi-observed values were finally used to determine biases for each model simulation.

Considering Eqs. (7) and (1) indicates that one component of the estimated evaporation is the estimated change in observed water store which, for month i is written b _i *P _i from Eq. (7). Now in the case of the model from which the b _i is estimated either underestimating snow-pack (observed in many of the models) or soil moisture (associated with the summer warm and dry bias seen in many of the models), if this is substantially less than underestimates of precipitation then the b _i will be too small. This implies that the estimated evaporation will also be too small and thus negative biases in evaporation underestimated.