In the framework of the EURO-CORDEX initiative an ensemble of European-wide high-resolution regional climate simulations on a 0.11 degrees (similar to 12.5 km) grid has been generated. This study investigates whether the fine-gridded regional climate models are found to add value to the simulated mean and extreme daily and sub-daily precipitation compared to their coarser-gridded 0.44 degrees (similar to 50 km) counterparts. Therefore, pairs of fine-and coarse-gridded simulations of eight reanalysis-driven models are compared to fine-gridded observations in the Alps, Germany, Sweden, Norway, France, the Carpathians, and Spain. A clear result is that the 0.11 degrees simulations are found to better reproduce mean and extreme precipitation for almost all regions and seasons, even on the scale of the coarser-gridded simulations (50 km). This is primarily caused by the improved representation of orography in the 0.11 degrees simulations and therefore largest improvements can be found in regions with substantial orographic features. Improvements in reproducing precipitation in the summer season appear also due to the fact that in the fine-gridded simulations the larger scales of convection are captured by the resolved-scale dynamics. The 0.11 degrees simulations reduce biases in large areas of the investigated regions, have an improved representation of spatial precipitation patterns, and precipitation distributions are improved for daily and in particular for 3 hourly precipitation sums in Switzerland. When the evaluation is conducted on the fine (12.5 km) grid, the added value of the 0.11 degrees models becomes even more obvious.
In: Tellus. Series A, Dynamic meteorology and oceanography, Vol. 67
There are well-known difficulties to run numerical weather prediction (NWP) and climate models at resolutions traditionally referred to as 'grey-zone' (similar to 3-8 km) where deep convection is neither completely resolved by the model dynamics nor completely subgrid. In this study, we describe the performance of an operational NWP model, HARMONIE, in a climate setting (HCLIM), run at two different resolutions (6 and 15 km) for a 10-yr period (1998-2007). This model has a convection scheme particularly designed to operate in the 'grey-zone' regime, which increases the realism and accuracy of the time and spatial evolution of convective processes compared to more traditional parametrisations. HCLIM is evaluated against standard observational data sets over Europe as well as high-resolution, regional, observations. Not only is the regional climate very well represented but also higher order climate statistics and smaller scale spatial characteristics of precipitation are in good agreement with observations. The added value when making climate simulations at similar to 5 km resolution compared to more typical regional climate model resolutions is mainly seen for the very rare, high-intensity precipitation events. HCLIM at 6 km resolution reproduces the frequency and intensity of these events better than at 15 km resolution and is in closer agreement with the high-resolution observations.
This report documents Coordinated Regional Downscaling Experiment (CORDEX) climate model simulations at 50 km horizontal resolution over Europe with the Rossby Centre regional atmospheric model (RCA4) for i) a ERA-Interim-driven (ERAINT) simulation used to evaluate model performance in the recent past climate, ii) historical simulations of the recent decades with forcing from nine different global climate models (GCMs) and iii) future scenarios RCP 4.5 and RCP 8.5 forced by the same nine different GCMs. Those simulations represent a subset of all CORDEX simulations produced at the Rossby Centre and a general conclusion drawn at the Rossby Centre is that such large ensembles could not have been produced without the establishment of an efficient production chain as outlined here.
The first part of this report documents RCA4 and its performance in a perfect boundary simulation where ERAINT was downscaled. RCA4 is to a large extent replicating the large-scale circulation in ERAINT, but some local biases in mean sea level pressure appear. In general the seasonal cycles of temperature and precipitation are simulated in relatively close agreement to observations. Some biases occur, such as too much precipitation in northern Europe and too little in the south. In winter, there is also too much precipitation in eastern Europe. Temperatures are generally biased low in northern Europe and in the Mediterranean region in winter while overestimated temperatures are seen in southeastern Europe in winter and in the Mediterranean area in summer. RCA4 performs generally well when simulating the recent past climate taking boundary conditions from the GCMs. A large part of the RCA4 simulated climate is attributed to the driving GCMs, but RCA4 creates its own climate inside the model domain and adds details due to higher resolution. All nine downscaled GCMs share problems in their representation of the large-scale circulation in winter. This feature is inherited in RCA4. The biases in large-scale circulation induce some biases in temperature and precipitation in RCA4.
The climate change signal in the RCP 4.5 and RCP 8.5 ensembles simulated by RCA4 is very similar to what has been presented previously. Both scenarios RCP 4.5 and RCP 8.5 project Europe to be warmer in the future. In winter the warming is largest in northern Europe and in summer in southern Europe. The summer maximum daily temperature increases in a way similar to summer temperature, but somewhat more in southern Europe. The winter minimum daily temperature in northern Europe is the temperature that changes the most. Precipitation is projected to increase in all seasons in northern Europe and decrease in southern Europe. The largest amount of rainfall per day (and per seven day period) is projected to increase in almost all of Europe and in all seasons. At the same time the longest period without precipitation is projected to be longer in southern Europe. Small changes in mean wind speed are generally projected. There are, however, regions with significant changes in wind.
The ensemble approach is a way to describe the uncertainties in the scenarios, but there are other possible ensembles using other models which would give other results. Still, the ensemble used here is found to be similar enough to these other possible ensembles to be representative of the whole set of GCMs. Dynamical downscaling using RCA4 changes the climate change signal, and the ensemble spread is sometimes reduced, but the ensemble of nine RCA4 simulations, using different GCMs, is considered to be representative of the full ensemble. All scenarios agree on a climate change pattern; the amplitude of the change is determined by the choice of scenario. The relative importance of the chosen scenario increases with time.
In: Science of the Total Environment, Vol. 514, 239-249 p.
Climate change is not only likely to improve conditions for crop production in Sweden, but also to increase weed pressure and the need for herbicides. This study aimed at assessing and contrasting the direct and indirect effects of climate change on herbicide leaching to groundwater in a major crop production region in south-west Sweden with the help of the regional pesticide fate and transport model MACRO-SE. We simulated 37 out of the 41 herbicides that are currently approved for use in Sweden on-eight major crop types for the 24 most common soil types in the region. The results were aggregated accounting for the fractional coverage of the crop and the area sprayed with a particular herbicide. For simulations of the future, we used projections of five different climate models as model driving data and assessed three different future scenarios: (A) only changes in climate, (B) changes in climate and land-use (altered crop distribution), and (C) changes in climate, land-use, and an increase in herbicide use. The model successfully distinguished between leachable and non-leachable compounds (88% correctly classified) in a qualitative comparison against regional-scale monitoring data. Leaching was dominated by only a few herbicides and crops under current climate and agronomic conditions. The model simulations suggest that the direct effects of an increase in temperature, which enhances degradation, and precipitation which promotes leaching, cancel each other at a regional scale, resulting ifs a slight decrease in leachate concentrations in a future climate. However, the area at risk of groundwater contamination doubled when indirect effects of changes in land-use and herbicide use, were considered. We therefore concluded that it is important to consider the indirect effects of climate change alongside the direct effects and that effective mitigation strategies and strict regulation are required to secure future (drinking) water resources. (C) 2014 Elsevier B.V. All rights reserved.
This chapter builds on the comprehensive summary of climate change scenarios in the first BACC assessment published in 2008. This chapter first addresses the dynamical downscaling of general circulation model (GCM) results to the regional scale, focussing on results from 13 regional climate model (RCM) simulations in the ENSEMBLES project as this European-scale ensemble simulation is also relevant for the Baltic Sea region and many studies on temperature, precipitation, wind speed and snow amounts have been performed. This chapter then reviews statistical downscaling studies that use large-scale atmospheric variables (predictors) to estimate possible future change in several smaller scale fields (predictands), with the greatest emphasis given to hydrological variables (such as precipitation and run-off). For the Baltic Sea basin, the findings of the statistical downscaling studies are generally in line with studies employing dynamical downscaling.
General (global) circulation models (GCMs) are a useful tool for studying how climate may change in the future. Although GCMs have high temporal resolution, their spatial resolution is low. To simulate the future climate of the Baltic Sea region, it is necessary to downscale GCM data. This chapter describes the two conceptually different ways of downscaling: regional climate models (RCMs) nested in GCMs and using empirical and/or statistical relations between large-scale variables from GCMs and small-scale variables. There are many uncertainties in climate models, including uncertainty related to future land use and atmospheric greenhouse gas concentrations, limits on the amount of input data and their accuracy, and the chaotic nature of weather. The skill of methods for describing regional climate futures is also limited by natural climate variability. For the Baltic Sea area, the lack of an oceanic component in RCMs and poor representation of forcing by aerosols and changes in land use are major limitations.
Models used for climate change impact projections are typically not tested for simulation beyond current climate conditions. Since we have no data truly reflecting future conditions, a key challenge in this respect is to rigorously test models using proxies of future conditions. This paper presents a validation framework and guiding principles applicable across earth science disciplines for testing the capability of models to project future climate change and its impacts. Model test schemes comprising split-sample tests, differential split-sample tests and proxy site tests are discussed in relation to their application for projections by use of single models, ensemble modelling and space-time-substitution and in relation to use of different data from historical time series, paleo data and controlled experiments. We recommend that differential-split sample tests should be performed with best available proxy data in order to build further confidence in model projections.