Guest Post “Modeled European Precipitation Change Smaller Than Observed” By Ronald van Haren, Geert Jan van Oldenborgh, Geert Lenderink andWilco Hazeleger


Modeled European precipitation change smaller than observed

by  Ronald van Haren, Geert Jan van Oldenborgh, Geert Lenderink, Wilco Hazeleger of the Royal Dutch Meteorological Institute (KNMI)


Now is an exciting time to do climate research. In many areas of the world climate change is emerging from the noise of natural variability. This opens the opportunity to compare the observed changes to the changes that are simulated by climate models. Climate models are mathematical representations of the climate system and should in principle give a physics-based response to increased concentrations of CO2 and other greenhouse gases, different types of aerosols, solar and volcanic forcings. However, many processes are too small-scale or complex to be physically represented in the model and are parameterized: the average or expected effect of such processes are specified. Examples are clouds, thunderstorms, fog, ocean mixing. The necessity to parameterize these processes adds model uncertainty into the simulations. Projections of the climate are also dependent on uncertainties in the forcings. Aerosol emissions and concentrations in the past are poorly known and future social-economic developments that affect emissions of greenhouse gases, aerosols and land use change are uncertain. Finally, we should always keep in mind that the climate system also shows natural variations on different timescales.

To deal with these uncertainties, use is often made of multiple climate models: a multi-model ensemble. The spread between the model results of such an ensemble is a combination of model uncertainty and natural climate variability. Note that even when natural variability is low, the model uncertainty is not equal to the spread of the ensemble. It can both be larger (if all models do not represent an essential process) or smaller (if the ensemble contains models of lower quality). For some models multiple realizations are available that allow an estimation of the natural variability from the spread within the model.

To come back to our goal: to have confidence in future climate projections, a correct representation of trends in the past is necessary (but not sufficient). In a recent article (van Haren et al, Clim.Dyn., 2012) we investigated if modeled changes in precipitation over Europe are in agreement with the observed changes.

Results & Discussion

Clear precipitation trends have been observed in Europe over the past century. In winter (October – March), precipitation has increased in north-western Europe. In summer (April – September), there has been an increase along many coasts in the same area. Over the second half of the past century precipitation also decreased in southern Europe in winter (figures 1a and 1d). We checked by comparing different analyses of precipitation that the difference between modeled and observed precipitation changes that are discussed in this article are much larger than the analysis uncertainty in the observations, except for some countries in eastern Europe that do not share much data. These analyses are partly based on the same station observations, but agreement between precipitation changes calculated over the second half of the past century and the complete past century give further confidence that the observed changes are physical and not artifacts of changes in the observational methods.

An investigation of precipitation trends in an ensemble of regional climate models (RCMs) of the ENSEMBLES project shows that these models fail to reproduce the observed trends (figures 1b and 1e). In many regions the observed trend is larger than in any of the models. Similar results are obtained for the entire last century in a comparison of the observed trends with trends in global climate models (GCMs) from the CMIP3 co-ordinated modeling experiment. The models should cover the full range of natural variability, so that the result that the observed trend is outside the ensemble implies that either the natural variability is underestimated, or the trend itself. We compared the natural variability over the last century between the models and observations. The GCMs were indeed found to underestimate the variability somewhat, but the RCMs actually overestimate natural variability on the interannual time scale. In Europe, there is very little evidence of low-frequency variability over land beyond the integrated effects of interannual variability: both the observations and the models are compatible with white noise once the trend has been subtracted.

We also have available from ENSEMBLES regional climate model experiments in which the large scale circulation and sea surface temperatures are prescribed from reanalysis data, which are close to the observations. These simulations reproduce the observed precipitation trends much better (figures 1c and 1f). The observed trends are largely compatible with the (smaller) range of uncertainties spanned by the ensemble, indicating that the prescribed factors in regional climate models, large scale circulation and sea surface temperatures, are responsible for large parts of the trend biases in the GCM-forced ensemble and the GCMs themselves.

Figure 1: Comparison of observed and modeled precipitation trends over 1961-2000 [%/century]. (a) Relative trends in observed summer precipitation. (b) Mean relative trends of summer precipitation of the GCM forced RCM ensemble. (c) Mean relative trends of summer precipitation of the RCM ensemble forced by reanalysis data. (d-f)

Using a simple statistical model we next investigated the relative importance of these two prescribed factors. We find that the main factor in setting the trend in winter is the large scale atmospheric circulation (as we found earlier for the winter temperature trends). The air pressure over the Mediterranean area has increased much stronger in the observations than in the models. In the summer season, sea surface temperature (SST) changes are important in setting precipitation trends along the North Sea and Atlantic coasts. Climate models underestimate the SST trends along the Atlantic coast, the North Sea and other coastal areas (if represented at all). This leads to lower evaporation trends and reduced trends in coastal precipitation.


The results of this study show that climate models are only partly capable of reproducing the details in observed precipitation changes: the local observed trends are often much larger than modeled in Europe. Because it is not clear (yet) whether the trend biases in SST and large scale circulation are due to greenhouse warming, their importance for future climate projections needs to be determined. Processes that give rise to the observed trends may very well be relatively unimportant for climate projection for the end of the century. Therefore, a straightforward extrapolation of observed trends to the future is not possible. A quantitative understanding of the causes of these trends is needed so that climate model based projections of future climate can be corrected for these trend biases.


–       Ronald van Haren, Geert Jan van Oldenborgh, Geert Lenderink, Matthew Collins and Wilco Hazeleger, SST and circulation trend biases cause an underestimation of European precipitation trends, Clim.Dyn, (2012) 10.1007/s00382-012-1401-5. preprint

–       G. J. van Oldenborgh, S. Drijfhout, A. van Ulden, R. Haarsma, A. Sterl, C. Severijns, W. Hazeleger, and H. Dijkstra, Western Europe is warming much faster than expected, Clim.Past, 5, 1-12, 2009. doi:10.5194/cp-5-1-2009, full text

–       van der Linden, P. and Mitchell, J. F. B. (Eds), ENSEMBLES: Climate Change and its Impacts: Summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, 2009. book

–       Meehl, Gerald A., Curt Covey, Karl E. Taylor, Thomas Delworth, Ronald J. Stouffer, Mojib Latif, Bryant McAvaney, John F. B. Mitchell, 2007: The WCRP CMIP3 Multimodel Dataset: A New Era in Climate Change Research. Bull. Amer. Meteor. Soc., 88, 1383–1394. doi: doi:10.1175/BAMS-88-9-1383. Full text

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