Utilizing reanalysis and high sensitivity W-band radar observations from CloudSat, this study assesses simulated high-latitude (55–82.5°) precipitation and its future changes under the RCP8.5 global warming scenario. A subset of models was selected based on the smallest discrepancy relative to CloudSat and ERA-I reanalysis using a combined ranking for bias and spatial root mean square error (RMSE). After accounting for uncertainties introduced by internal variability due to CloudSat’s limited four year day-night observation period, RMSE provides greater discrimination between the models than a typical mean state bias criterion. Over 1976–2005 to 2071–2100, colder months experience larger fractional modelled precipitation increases than warmer months, and the observation-constrained models generally report a larger response than the full ensemble. For everywhere except the Southern Hemisphere (SH55, for 55–82.5°S) ocean, the selected models show greater warming than the model ensemble while their hydrological sensitivity (fractional precipitation change with temperature) is indistinguishable from the full ensemble relationship. This indicates that local thermodynamic effects explain much of the net high-latitude precipitation change. For the SH ocean, the models that perform best in the present climate show near-median warming but greater precipitation increase, implying a detectable contribution from processes other than local thermodynamic changes. A Taylor diagram analysis of the full CMIP5 ensemble finds that the Northern Hemisphere (NH55) and SH55 land areas follow a “wet get wetter” paradigm. The SH55 land areas show stable spatial correlations between the simulated present and future climate, indicative of small changes in the spatial pattern, but this is not true of NH55 land. This shows changes in the spatial pattern of precipitation changes through time as well as the differences in precipitation between wet and dry regions.
Design and Analysis of a Micromechanical Three-Component Force Sensor for Characterizing and Quantifying Surface Roughness
