Precipitation changes projected for the end of the twenty-first century show an increase of more than 50 per cent in the Arctic regions. This marked increase, which is among the highest globally, has previously been attributed primarily to enhanced poleward moisture transport from lower latitudes. Here we use state-of-the-art global climate models to show that the projected increases in Arctic precipitation over the twenty-first century, which peak in late autumn and winter, are instead due mainly to strongly intensified local surface evaporation (maximum in winter), and only to a lesser degree due to enhanced moisture inflow from lower latitudes (maximum in late summer and autumn). Moreover, we show that the enhanced surface evaporation results mainly from retreating winter sea ice, signalling an amplified Arctic hydrological cycle. This demonstrates that increases in Arctic precipitation are firmly linked to Arctic warming and sea-ice decline. As a result, the Arctic mean precipitation sensitivity (4.5 per cent increase per degree of temperature warming) is much larger than the global value (1.6 to 1.9 per cent per kelvin). The associated seasonally varying increase in Arctic precipitation is likely to increase river discharge and snowfall over ice sheets (thereby affecting global sea level), and could even affect global climate through freshening of the Arctic Ocean and subsequent modulations of the Atlantic meridional overturning circulation.

Abstract. The main characteristics of the new version 1.2 of the three-dimensional Earth system model of intermediate complexity LOVECLIM are briefly described. LOVE-CLIM 1.2 includes representations of the atmosphere, the ocean and sea ice, the land surface (including vegetation), the ice sheets, the icebergs and the carbon cycle. The atmospheric component is ECBilt2, a T21, 3-level quasigeostrophic model. The ocean component is CLIO3, which consists of an ocean general circulation model coupled to a comprehensive thermodynamic-dynamic sea-ice model. Its horizontal resolution is of 3 • by 3 • , and there are 20 levels in the ocean. ECBilt-CLIO is coupled to VECODE, a vegetation model that simulates the dynamics of two main terrestrial plant functional types, trees and grasses, as well as desert. VECODE also simulates the evolution of the carbon cycle over land while the ocean carbon cycle is represented by LOCH, a comprehensive model that takes into acCorrespondence to: H. Goosse (hugues.goosse@uclouvain.be) count both the solubility and biological pumps. The ice sheet component AGISM is made up of a three-dimensional thermomechanical model of the ice sheet flow, a visco-elastic bedrock model and a model of the mass balance at the iceatmosphere and ice-ocean interfaces. For both the Greenland and Antarctic ice sheets, calculations are made on a 10 km by 10 km resolution grid with 31 sigma levels. LOVECLIM1.2 reproduces well the major characteristics of the observed climate both for present-day conditions and for key past periods such as the last millennium, the mid-Holocene and the Last Glacial Maximum. However, despite some improvements compared to earlier versions, some biases are still present in the model. The most serious ones are mainly located at low latitudes with an overestimation of the temperature there, a too symmetric distribution of precipitation between the two hemispheres, and an overestimation of precipitation and vegetation cover in the subtropics. In addition, the atmospheric circulation is too weak. The model also tends to underestimate the surface temperature changes (mainly at low latitudes) and to overestimate the ocean heat uptake observed over the last decades.

As an alternative to the frequently used mixed boundary conditions in ocean GCM's, we present a dynamic atmospheric model (ECBILT) that is simple and yet describes the relevant dynamic and thermodynamic feedback processes to the ocean. This provides the possibility of studying atmosphere/ocean dynamics on very long time‐scales of the order of a thousand years. The model is two orders of magnitude faster than AGCMs. We have been running ECBILT with prescribed SSTs for a period of 500 years with seasonal cycle included both in the solar forcing and in the climatological SSTs. The mean state and the variability properties of ECBILT are reasonably realistic. The simulation of the surface fluxes is quite realistic and justifies coupling ECBILT to an ocean GCM. We have done two SST anomaly experiments, one with a tropical SST anomaly as observed during January 1983 and one with an SST anomaly in the northern extra‐tropical Atlantic ocean. For the tropical SST anomaly experiment the amount of anomalous precipitation agrees well with what has been found with atmospheric GCM's, but the resulting extra‐tropical teleconnection pattern is very weak. The atmospheric response pattern to extra‐tropical SST anomalies agrees well with similar SST anomaly experiments performed with atmospheric GCM's. We have tested the performance of ECBILT in coupled mode by coupling it to a simple ocean GCM and thermodynamic sea‐ice model and integrating the coupled system for a period of thousand years after a spin up of 6000 years. The simulation of the mean water mass distribution and the mean circulation of the ocean resembles the observed ocean circulation. It has a warm and fresh bias and the circulation and associated transports are too diffuse and too weak. The ocean's variability is realistic, considering the simplicity of the ocean model, although a bit too weak. We have explored the covariability between the atmosphere and ocean over the Northern Atlantic ocean by performing a singular value decomposition of SST anomalies and 800 hPa geopotential height anomalies. The 2nd mode shows a peak in both spectra at a timescale of about 18 years. The time scale of this mode is set by the ocean but the physical mechanisms that are operating are not yet unambiguously identified. The simulation of this coupled extra tropical decadal mode of variability, which also shows up in the observations and in much more complex coupled models provides strong evidence for the potential usefullness of ECBILT when studying atmosphere/ocean interaction and the associated ocean variability on time scales ranging from decades to many thousands of years.

T he ChAllenge. Climate and weather forecasting applications share a common ancestry and build on the same physical principles. Nevertheless, climate research and numerical weather prediction (NWP) are commonly seen as different disciplines. The emerging concept of "seamless prediction" forges weather forecasting and climate change studies into a single framework. At the same

[1] The NCEP/NCAR re-analyses as well as ensemble integrations with an atmospheric GCM indicate that interannual variations in Sahel rainfall are related to variations in the mean sea level pressure (MSLP) over the Sahara. In turn the MSLP variations are related to the global distribution of surface air temperature (SAT). An increase in SAT over the Sahara, relative to the surrounding oceans, decreases the MSLP over the Sahara, thereby increasing the Sahel rainfall. We hypothesize that through this mechanism greenhouse warming will cause an increase in Sahel rainfall, because the warming is expected to be more prominent over the summer continents than over the oceans. This has been confirmed using an ensemble of 62 coupled model runs forced with a business as usual scenario. The ensemble mean increase in Sahel rainfall between 1980 and 2080 is about 1 -2 mm day À1 (25 -50%) during July -September, thereby strongly reducing the probability of prolonged droughts. Citation: Haarsma, R. J., F. M. Selten, S. L. Weber, and M. Kliphuis (2005), Sahel rainfall variability and response to greenhouse warming, Geophys. Res. Lett., 32, L17702,

The growing share of variable renewable energy increases the meteorological sensitivity of power systems. This study investigates if large-scale weather regimes capture the influence of meteorological variability on the European energy sector. For each weather regime, the associated changes to wintertime-mean and extreme-wind and solar power production, temperature-driven energy demand and energy shortfall (residual load) are explored. Days with a blocked circulation pattern, i.e. the 'Scandinavian Blocking' and 'North Atlantic Oscillation negative' regimes, on average have lower than normal renewable power production, higher than normal energy demand and therefore, higher than normal energy shortfall. These average effects hide large variability of energy parameters within each weather regime. Though the risk of extreme high energy shortfall events increases in the two blocked regimes (by a factor of 1.5 and 2.0, respectively), it is shown that such events occur in all regimes. Extreme high energy shortfall events are the result of rare circulation types and smaller-scale features, rather than extreme magnitudes of common large-scale circulation types. In fact, these events resemble each other more strongly than their respective weather regime mean pattern. For (sub-) seasonal forecasting applications weather regimes may be of use for the energy sector. At shorter lead times or for more detailed system analyses, their ineffectiveness at characterising extreme events limits their potential.

Abstract. The low-lying Netherlands is at risk from multiple threats of sea level rise, storm surges and extreme river discharges. Should these occur simultaneously, a catastrophe will be at hand. Knowledge about the likelihood of simultaneous occurrence or the so-called "compound effect" of such threats is essential to provide guidance on legislation for dike heights, flood barrier design and water management in general. In this study, we explore the simultaneous threats of North Sea storm surges and extreme Rhine river discharge for the current and future climate in a large 17-member global climate model ensemble. We use a simple approach, taking proxies of north-northwesterly winds over the North Sea and multiple~day precipitation averaged over the Rhine basin for storm surge and discharge respectively, so that a sensitivity analysis is straightforward to apply. By investigating soft extremes, we circumvent the need to extrapolate the data and thereby permit the model's synoptic development of the extreme events to be inspected. Our principle finding based on the climate model data is that, for the current climate, the probability of extreme surge conditions following extreme 20-day precipitation sums is around 3 times higher than that estimated from treating extreme surge and discharge probabilities as independent, as previously assumed. For the future climate (2070–2100), the assumption of independence cannot be rejected, at least not for precipitation sums exceeding 7 days.

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