Validation and analysis of regional present-day climate and climate change simulations over Europe

In the European Commission (EC) project "Regionalization of Anthropogenic Climate Change Simulations, RACCS, recently terminated, 11 European institutions have carried out tests of dynamical and statistical regionalization techniques. The outcome of the "dynamical part" of the project, utilizing a series of high resolution LAMs and a variable resolution global model (all of which we shall refer to as RCMs, Regional Climate Models), is presented here. The per- formance of the dqterent LAMs had first, in a preceding EC project, been tested with "perfect" boundary forcing fields (ECMWF analyses) and also multi-year present-day climate simula- tions with AMIP "perfect ocean " or mixed layer ocean GCM boundary conditions had been validated against available climatological data. The present report involves results of vali- dation and analysis of RCM present-day climate simulations and anthropogenic climate change experiments. Multi-year (5 - 30 years) present-day climate simulations have been per- formed with resolutions between 19 and 70 km (grid lengths) and with boundary conditions from the newest CGCM simulations. The climate change experiments involve various 2xCO2 - ]xCO2 transient greenhouse gas experiments and in one case also changing sulphur aerosols. A common validation and inter-comparison was made at the coordinating institution, MPIfor Meteorology. The validation of the present-day climate simulations shows the importance of systematic errors in the low level general circulation. Such errors seem to induce large errors in precipitation and surface air temperature in the RCMs as well as in the CGCMs providing boundary conditions. Over Europe the field of systematic errors in the mean sea level pressure (MSLP) usually involve an area of too low pressure, often in the form of an east-west trough across Europe with too high pressure to the north and south. New storm-track analyses confirm that the areas of too low pressure are caused by enhanced cyclonic activity and similarly that the areas of too high pressure are caused by reduced such activity. The precise location and strength of the extremes in the MSLP error field seems to be dependent on the physical param- eterization package used. In model pairs sharing the same package the area of too low pressure is deepened further in the RCM compared to the corresponding CGCM, indicating an increase of the excessive cyclonic activity with increasing resolution. From the experiments performed it seems not possible to decide to what extent the systematic errors in the general circulation are the result of local errors in the physical parameterization schemes or remote errors trans- mitted to the European region via the boundary conditions. Additional errors in precipitation and temperature seems to be due to direct local effects of errors in certain parameterization schemes and errors in the SSTs taken from the CGCMs. For all seasons many biases are fOund to be statistically significant compared to estimates of the internal model variability of the time- slice mean values. In the climate change experiments statistically significant European mean temperature changes which are large compared to the corresponding biases are found. How- ever, the changes in the deviations from the European mean temperature as well as the changes in precipitation are only partly sign wcan ce and are of the same order of magnitude or smaller than the corresponding biases found in the present-day climate simulations. Cases of an inter- action between the systematic model errors and the radiative forcing show that generally the errors are not canceling out when the changes are computed. Therefore, reliable regional cli- mate changes can only be achieved after model improvements which reduce their systematic errors sufficiently. Also in future RCM experiments sujiciently long time-slices must be used in order to obtain statistically sign ijicant climate changes on the sub-continental scale aimed at with the present regionalization technique

The future of tundra carbon storage in Greenland:sensitivity to climate and plant trait changes

Abstract The continuous change in observed key indicators such as increasing nitrogen deposition, temperatures and precipitation will have marked but uncertain consequences for the ecosystem carbon (C) sink-source functioning of the Arctic. Here, we use multiple in-situ data streams measured by the Greenland Ecosystem Monitoring programme in tight connection with the Soil-Plant-Atmosphere model and climate projections from the high-resolution HIRHAM5 regional model. We apply this modelling framework with focus on two climatically different tundra sites in Greenland (Zackenberg and Kobbefjord) to assess how sensitive the net C uptake will expectedly be under warmer and wetter conditions across the 21st century and pin down the relative contribution to the overall C sink strength from climate versus plant trait variability. Our results suggest that temperatures (5–7.7 °C), total precipitation (19–110 %) and vapour pressure deficit will increase (32–36 %), while shortwave radiation will decline (6–9 %) at both sites by 2100 under the RCP8.5 scenario. Such a combined effect will, on average, intensify the net C uptake by 9–10 g C m−2 year−1 at both sites towards the end of 2100, but Zackenberg is expected to have more than twice the C sink strength capacity of Kobbefjord. Our sensitivity analysis not only reveals that plant traits are the most sensitive parameters controlling the net C exchange in both sites at the beginning and end of the century, but also that the projected increase in the net C uptake will likely be similarly influenced by future changes in climate and existing local nutrient conditions. A series of experiments forcing realistic changes in plant nitrogen status at both sites corroborates this hypothesis. This work proves the unique synergy between monitoring data and numerical models to assist robust model calibration/validation and narrow uncertainty ranges and ultimately produce more reliable C cycle projections in understudied regions such as Greenland