Abstract. Commonly used bias correction methods such as quantile mapping (QM) assume the function of error correction values between modeled and observed distributions are stationary or time invariant. This article finds that this function of the error correction values cannot be assumed to be stationary. As a result, QM lacks justification to inflate/deflate various moments of the climate change signal. Previous adaptations of QM, most notably quantile delta mapping (QDM), have been developed that do not rely on this assumption of stationarity. Here, we outline a methodology called scaled distribution mapping (SDM), which is conceptually similar to QDM, but more explicitly accounts for the frequency of rain days and the likelihood of individual events. The SDM method is found to outperform QM, QDM, and detrended QM in its ability to better preserve raw climate model projected changes to meteorological variables such as temperature and precipitation.
EPA reports a steady decline of US anthropogenic NO x emissions in 2005–2019 summers, while NO2 vertical column densities (VCDs) from the OMI satellite over large spatial domains have flattened since 2009. To better understand the contributing factors to a flattening of the OMI NO2 trends, we investigate the role of soil and lightning NOx emissions on this apparent disagreement. We improve soil NO x emissions estimates using a new observation-based temperature response, which increases the linear correlation coefficient between GEOS-Chem simulated and OMI NO2 VCDs by 0.05–0.2 over the Central US. Multivariate trend analysis reveals that soil and lightning NO x combined emissions trends change from −3.95% a−1 during 2005–2009 to 0.60% a−1 from 2009 to 2019, thereby rendering the abrupt slowdown of total NO x emissions reduction. Non-linear inter-annual variations explain 6.6% of the variance of total NO x emissions. As background emissions become relatively larger with uncertain inter-annual variations, the NO2 VCDs alone at the national scale, especially in the regions with vast rural areas, will be insufficient to discern the trend of anthropogenic emissions.
<p>Using high-spatial-resolution regional simulations from the global program, Coordinated Regional Climate Downscaling Experiment-Coordinated Output for Regional Evaluations (CORDEX-CORE), we examine the capability of regional climate models (RCMs) to represent the El Ni&#241;o&#8211;Southern Oscillation (ENSO) precipitation and surface air temperature teleconnections during boreal winter (December-February). This study uses CORDEX-CORE simulations for the period 1975-2004 with two RCMs, the RegCM4 and REMO, driven by three General Circulation Models (GCMs) from phase 5 of the Coupled Model Inter-comparison Project (CMIP5). The RCM simulations were run at a 25-km grid spacing over Africa, Central and North America, South Asia and South America.</p><p>The teleconnection patterns are calculated in the reanalysis data (observations), and these results are compared to those of the ensemble and individual simulations of both the GCM and RCM. Linear regression is used to calculate the teleconnection patterns and a permutation test is applied to calculate the statistical significance of the regression coefficients. Results show that overall, the ENSO signal from the GCMs is preserved in the ensemble and the individual RCM simulations over most of the regions analyzed. These reproduced most of the observed regional responses to ENSO forcing and showing teleconnection signals statistically significant at the 95% level. Furthermore, in some cases, the ensemble and individual simulations of RCMs improve the spatial pattern and the amplitude of the ENSO precipitation response of the GCMs, particularly over southern Africa, the Arabian-Asian region, and the region composed of Mexico and the southern United States. These results show the potential value of the GCM-RCM downscaling systems not only in the context of climate change research but also for seasonal to annual prediction.</p>
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