Evaluation of modeled changes in extreme precipitation in Europe and the Rhine basin

Estimates of future changes in extremes of multi-day precipitation sums are critical for estimates of future discharge extremes of large river basins and changes in frequency of major flooding events (Kew et al 2010). A correct representation of past changes is an important (but not sufficient) condition to have confidence in projections for the future.

High discharge rates for the Rhine in the Netherlands usually occur in (late) winter (Rijkswaterstaat Waterdienst 2012). Evaporation rates in winter are low and soils are often saturated and sometimes frozen. Rainfall has the potential to melt large amounts of snow by bringing large amounts of thermal energy to the snowpack, increasing runoff (Disse and Engel 2001). Over the past century the average (Disse and Engel 2001) and extreme (Wang et al 2005) discharge of the Rhine in winter increased. Model results project a further increase for the current century (e.g. Hurkmans et al 2010, Lenderink et al 2007, Kew et al 2010, te Linde et al 2010), mainly caused by an increase in precipitation (van Pelt et al 2012) and a shift of the snowmelt season from spring to winter (Barnett et al 2005).

The climate in Europe depends strongly on the atmospheric circulation. Western circulation brings moist air from the Atlantic to the continent (van Ulden and van Oldenborgh 2006), leading to increasing precipitation over the continent. In an earlier paper we concluded that a misrepresentation of circulation changes in climate models is responsible for the underestimation of increase in winter precipitation in northwest Europe over the past century in climate models (van Haren et al 2013). Recent research finds that higher quantiles of daily precipitation correlate well with mean precipitation (Benestad et al 2012) and that the increase in mean winter extreme precipitation in Europe is similar across a range of multi-day sums (Kew et al 2010). The inability of climate models to capture the observed trend in atmospheric circulation could, through transport of moisture, also influence trends in extreme precipitation: particular circulation types may be more favorable for extreme precipitation events to occur.

In this paper we investigate whether the spread of climate models (which includes natural variability) covers the observed increase in extreme precipitation in Europe and the Rhine basin in late winter. We evaluate modeled trends in extreme precipitation, and try to find causes for the difference in trends. We only consider the uncertainty in trends of extreme precipitation. Estimates of trends in river discharge requires the coupling with a hydrological model of the catchment area and hydraulic models of the river and its main branches (e.g. Lenderink et al 2007), which is outside the scope of this study.

2.1. Study area

We study the extreme precipitation in Europe and the Rhine area (indicated by the area in figure 1(a)). The Rhine is the longest river in western Europe, originating in the Swiss Alps. From Switzerland it flows through the principality of Liechtenstein, Austria, Germany and France before it enters the Netherlands near Lobith, on the Dutch–German border. The main flow reaches the sea near Rotterdam, the Netherlands. The drainage area of the Rhine is approximately 185 000 km².

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Natural variability plays an important role in determining trends of extreme events. To give an impression of the magnitude of the inter-annual variability, we show in figure 1(b) the observed and modeled time-series (with accompanying trend estimates) of annual maxima January–March (JFM) 10 day running precipitation sums averaged over the Rhine basin. The modeled series is only shown for one climate model (EC-EARTH, 1 member). Strong inter-annual variability is found for both observed and modeled time-series, although with a larger (absolute) magnitude for the observed series.

In late winter precipitation in Europe is mainly caused by frontal systems from the Atlantic. The mean precipitation decreases from the coast and is highest on the west side of mountain ranges. The latitude where most rain falls is determined by the zonal pressure difference, with a blocking high over northern Europe causing dry weather there and more rain in southern Europe. Conversely, a stronger westerly flow brings more rain to northern Europe and less to the Mediterranean area.

2.2. Analysis period

2.3. Datasets

3.1. Effect of circulation change

To investigate if the change in mean circulation characteristics affects the change in precipitation extremes, we fit a simple statistical model that isolates the linear effect of mean circulation anomalies (van Ulden and van Oldenborgh 2006, van Oldenborgh et al 2009, van Haren et al 2013) to the anomalies of the annual maxima series of RX10day, P'(x,y,t). This is done for each dataset separately. These effects include the influence of mean geostrophic wind anomalies U_g(x,y,t),V_g(x,y,t) and vorticity anomalies ${G}_{\mathrm{vorticity}}^{\prime}(x,y,t)$ and the remaining noise η(x,y,t). The longitude and latitude of the grid box is indicated by (x,y),t indicates the time.

$$\begin{eqnarray} &&\fl {P}^{\prime}(x,y,t)={P}_{\mathrm{circ}}^{\prime}(x,y,t)+{P}_{\mathrm{ residual}}^{\prime}(x,y,t) \end{eqnarray} \tag{ 1 }$$

$$\begin{eqnarray} &&\fl {P}_{\mathrm{circ}}^{\prime}(x,y,t)={B}_{\mathrm{ W}}(x,y){U}_{\mathrm{g}}(x,y,t)\nonumber\\ &&+\, {B}_{\mathrm{S}}(x,y){V}_{\mathrm{g}}(x,y,t)+{B}_{\mathrm{V}}(x,y){G}_{\mathrm{vorticity}}^{\prime}(x,y,t) \end{eqnarray} \tag{ 2 }$$

$$\begin{eqnarray} &&\fl {P}_{\mathrm{residual}}^{\prime}(x,y,t)=A(x,y)t+\eta (x,y,t). \end{eqnarray} \tag{ 3 }$$

The geostrophic wind anomalies and vorticity anomalies are computed from the monthly NCEP/NCAR Reanalysis sea-level pressure (SLP) dataset (NCEP/NCAR, Kistler et al 2001) when considering observed precipitation anomalies, but other SLP datasets (Twentieth Century Reanalysis (20C, Compo et al 2011), Trenberth Northern Hemisphere SLP (Trenberth and Paolino 1980)) give similar results. Model SLP output is used in combination with modeled precipitation anomalies. The coefficients B_W(x,y),B_S(x,y),B_V(x,y) and A(x,y) are fitted over 1950–2012 for each 3 month winter period.

3.2. Trend definition

Our approach is to fit a non-stationary generalized extreme value (GEV) distribution to the annual maxima series of RX10day, as well as separately to the derived circulation and residual components. The GEV distribution is described as (e.g. Katz et al 2002)

$$\begin{eqnarray} &&\fl F(x;\mu ,\sigma ,\gamma )\nonumber\\ &&=\left \{ \begin{array}{@{}l@{}} \displaystyle \exp \{ -[1+\gamma (x-\mu )/\sigma ]^{-1/\gamma }\} ,\tqs \\\tqs \displaystyle \text{for}~\{ 1+\gamma (x-\mu )/\sigma \gt 0,~\gamma \not = 0\} \\ \displaystyle \exp \{ -\exp [-(x-\mu )/\sigma ]\} ,\tqs \text{for}~\gamma =0 \end{array}\right . \end{eqnarray} \tag{ 4 }$$

where μ,σ > 0, and γ are the location, scale and shape parameters, respectively. We adopted the homoscedastic model (constant variance) where the location parameter is described by

$$\begin{eqnarray} &&\mu (t)={\mu }_{0}+{\mu }_{1}t \end{eqnarray} \tag{ 5 }$$

here μ₁ is a trend in the location parameter, and t is a covariate linear in time from 1950–2012. Whenever we refer to a trend in precipitation extremes in later sections we refer to the trend as estimated by the μ₁ parameter. The scale and shape parameters are constant in the homoscedastic model. Experiments allowing for a time-varying scale parameter produced similar trends in the location parameter.

The parameters are estimated by the maximum likelihood method because of its ability to estimate time dependent covariates as recommended by Katz et al (2002) and Kharin and Zwiers (2005).

3.3. Rank histograms

Precipitation extremes in late winter have increased in the northern half of Europe in the last 60 years. In figure 2 we show the observed (a) and modeled (b) trend in 10 day annual maxima (RX10day) for late winter between 1950–2012. Externally forced changes should appear in the mean trend of the model ensemble (figure 2(b)). If no trend is found here, the observed trend is either caused by natural variability or climate models fail to (correctly) represent processes that are important for precipitation. The figures show that the average modeled trend in northern Europe is much weaker than the observed trend. The trend bias in the models is significant in northern Europe: figure 2(c) shows that for a lot of grid points in this area the observed trend is larger than in any of the models (dark red color). A summary of this panel is given by the rank histogram in figure 2(d) (because of larger uncertainties in the observations in eastern Europe these are calculated over the area west of 20°E), which shows that the underestimation of positive trends is significant at the 90% confidence interval: i.e. climate models likely underestimate natural variability or have common errors in processes that are important for precipitation. Another explanation could be that the quality of the observations is not good enough or the station network density is not high enough to compute reliable trends (Haylock et al 2008, Hofstra et al 2010). However, the decorrelation scale of winter precipitation is larger than the inter-station distance in most of Europe (except in the far North). The relatively low horizontal grid spacing (1.5°) used when evaluating model results further reduces the influence of interpolation and station network density on the trends. A dedicated study to (extreme) precipitation trends in the Netherlands produces similar trends with a homogenized dataset (Buishand et al 2012), giving more confidence in the quality of the observations themselves.

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The underestimation of the trend in extreme precipitation could originate from a number of possible causes: (1) the coarse resolution of global climate models may not be enough to describe extreme precipitation events, or may not provide enough detail on local conditions such as topography and coastlines that could affect modeled precipitation; (2) climate models underestimate natural variability of extreme precipitation; (3) underestimation in change of mean circulation characteristics.; (4) model errors (unrelated to atmospheric circulation) present in both GCMs and RCMs; (5) observational errors. We investigate the contribution of points 1–3 to the underestimation of trends in extreme precipitation. The contributions of points 4 and 5 are part of the remaining error budget.

To investigate if the trend bias is caused by the coarse resolution of GCMs we performed the same analysis with a multi-model ensemble of RCMs. We used the ensemble provided by Research Theme 2b of the European ENSEMBLES project, RT2b. The RCM ensemble has, with exception of details, similar trend biases as the ensemble composed of GCMs (not shown).

A second reason for the low reliability of climate models could be an underestimation of natural variability of extreme precipitation in climate models. Year-to-year natural variability is indeed underestimated in GCMs (standard error of the trend estimate for the models is smaller than for the observations), but this is not the case for RCMs (not shown). It is therefore unlikely that the trend bias is caused by an underestimation of natural variability on short time scales in the models. Underestimation of natural variability on multi-decadal or longer time scales could still be possible.

As discussed in section 1, particular circulation types may be more favorable for extreme precipitation events to occur. Using the statistical model defined by equations (1)–(3) we estimate atmospheric circulation induced precipitation changes. The observed/modeled circulation dependent trend, within the linear approximation of equations (1)–(3), is given in figures 2(e) and (f). The residual part of the trend that is not linearly dependent on circulation changes is given in panels (i) and (j).

Seasonal mean circulation changes (figure 3(a), the pattern is consistent over NCEP/NCAR, 20C and Trenberth) cause an increase in observed extreme precipitation in parts of central and northern Europe, as well as a decrease in much of southern Europe. The CMIP5 models also show a north–south dipole in the pressure trends over continental Europe, although on average much weaker and more southeasterly displaced (and a trend to lower pressures over Greenland). Although, this pressure change also causes a (slightly displaced) north–south precipitation response, the response is too weak to show up in figure 2(e). Figure 2(g) (with a summary in 2(h)) shows the fraction of the CMIP5 models with a circulation dependent trend larger than the observed circulation dependent trend. The observed circulation dependent trend caused by a change in geostrophic winds is larger than in any of the models for large parts of northern (increase in precipitation) and southern (decrease in precipitation) Europe. For northern Europe this shows up in an underestimation of the total trend by the models. The circulation dependent decrease in southern Europe is (partly) canceled out by an increase due to other factors. Inconsistencies in the underlying data provides low confidence in the results in the Balkan area. We did not find obvious problems in the Iberian Peninsula so we would have to assume that the (partial) cancelation is real in this area.

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A possible explanation for the residual trend in the far north and in eastern Europe is a strong temperature increase in these regions. When the temperature increases, so does the water-holding capacity of the atmosphere, which in turn favors stronger rainfall events (IPCC 2007). An alternative explanation is that decreasing sea ice extent results in more open water, increasing evaporation.

We consider the trend in extreme 10 day precipitation over the Rhine basin (figure 1(a)). These trends are important for flood risk management in the Netherlands.

Figure 4(a) shows the observed and modeled trends in JFM RX10day as averaged over the Rhine basin area. The trend is calculated as the trend of the area average. The observed trend lies on the outer edge of the spread of modeled trends. We verified the results in an ensemble of regional climate models forced by global models (European ENSEMBLES RT2b), producing similar results (not shown).

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A large part of the trend in this region is, within the linear approximation of a statistical decomposition, caused by a change in circulation (figure 2). In a similar manner as before, we estimate the atmospheric circulation induced precipitation changes for the whole basin, where the averaged change in geostrophic wind anomalies over the basin area is used. We find that a large part of the observed trend is linearly related to changes in mean atmospheric circulation. For the models the effect of circulation is much smaller. Adjusting for the circulation trend mismatch by only considering the residual component, the observed trend is well within the climate model ensemble (figure 4(b)).

Climate model based projections of future precipitation extremes are often used in projections of future river discharge extremes. Here, the trends in extreme precipitation in Europe and the Rhine river basin over the last 60 years are compared with observed trends. A correct representation of past changes is an important (but not sufficient) condition to have confidence in projections for the future.

We find that climate models underestimate the trend in extreme precipitation in the northern half of Europe: the trend bias is significant in this area. Using a statistical decomposition we split the trend in a part that is linearly related to circulation change, and a residual part that is not linearly related to circulation change. Circulation changes have caused an increase in observed extreme precipitation in parts of mid and northern Europe, as well as a decrease over the Iberian Peninsula. Climate models underestimate the change in circulation over the past century and as a result have a much smaller (extreme) precipitation response.

Climate models are not capable of reproducing the observed trend in extremes for the Rhine basin. The underestimation is, within the linear approximation of a statistical decomposition and statistical uncertainties, caused by an underestimation of the change in mean circulation. The ensemble covers the observed trend when only the part of the trend not linearly related to mean circulation change is considered: the residual biases not linearly related to mean circulation changes are relatively small. Therefore, it is important that we improve our understanding of circulation changes, in particular related to the cause of the apparent mismatch between observed and modeled circulation trends over the past century (Haarsma et al 2013).

The research was supported by the Dutch research program Knowledge for Climate.