RESUMO
A large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistical downscaling model capable of replicating extreme events, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), to downscale daily precipitation, air-temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). BCCAQ is calibrated using high-resolution reference datasets and showed a good performance in removing bias from GCMs and reproducing extreme events. The globally downscaled data are available at the Centre for Environmental Data Analysis ( https://doi.org/10.5285/c107618f1db34801bb88a1e927b82317 ) for the historical (1981-2014) and future (2015-2100) periods at 0.25° resolution and at daily time step across three Shared Socioeconomic Pathways (SSP2-4.5, SSP5-3.4-OS and SSP5-8.5). This new climate dataset will be useful for assessing future changes and variability in climate and for driving high-resolution impact assessment models.
RESUMO
We present the first simultaneous space-time measurements for gravity wave turbulence in a large laboratory flume. We found that the slopes of k and omega wave spectra depend on wave intensity. This cannot be explained by any existing theory considering wave turbulence as the result of either breaking events or weakly nonlinear wave interactions. Instead, we show that random waves and breaking or coherent structures appear to coexist: The former show themselves in a quasi-Gaussian core of the probability density function and in the low-order structure functions, and the latter in the probability density function tails and the high-order structure functions.