Simulation and Evaluation of Statistical Downscaling of Regional Daily Precipitation over North China Based on Self-Organizing Maps

A statistical downscaling method based on Self-Organizing Maps (SOM), of which the SOM Precipitation Statistical Downscaling Method (SOM-SD) is named, has received increasing attention. Herein, its applicability of downscaling daily precipitation over North China is evaluated. Six indices (total season precipitation, daily precipitation intensity, mean number of precipitation days, percentage of rainfall from events beyond the 95th percentile value of overall precipitation, maximum consecutive wet days, and maximum consecutive dry days) are selected, which represent the statistics of daily precipitation with regards to both precipitation amount and frequency, as well as extreme event. The large-scale predictors were extracted from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) daily reanalysis data, while the prediction was the high resolution gridded daily observed precipitation. The results show that the method can establish certain conditional transformation relationships between large-scale atmospheric circulation and local-scale surface precipitation in a relatively simple way. This method exhibited a high skill in reproducing the climatologic statistical properties of the observed precipitation. The simulated daily precipitation probability distribution characteristics can be well matched with the observations. The values of Brier scores are between 0 and 1.5 × 10−4 and the significance scores are between 0.8 and 1 for all stations. The SOM-SD method, which is evaluated with the six selected indicators, shows a strong simulation capability. The deviations of the simulated daily precipitation are as follows: Total season precipitation (−7.4%), daily precipitation intensity (−11.6%), mean number of rainy days (−3.1 days), percentage of rainfall from events beyond the 95th percentile value of overall precipitation (+3.4%), maximum consecutive wet days (−1.1 days), and maximum consecutive dry days (+3.5 days). In addition, the frequency difference of wet-dry nodes is defined in the evaluation. It is confirmed that there was a significant positive correlation between frequency difference and precipitation. The findings of this paper imply that the SOM-SD method has a good ability to simulate the probability distribution of daily precipitation, especially the tail of the probability distribution curve. It is more capable of simulating extreme precipitation fields. Furthermore, it can provide some guidance for future climate projections over North China.

Downscaling of Future Precipitation in China’s Beijing-Tianjin-Hebei Region Using a Weather Generator

To project local precipitation at the existing meteorological stations in China’s Beijing-Tianjin-Hebei region in the future, local daily precipitation was simulated for three periods (2006–2030, 2031–2050, and 2051–2070) under RCP 4.5 and RCP 8.5 emission scenarios. These projections were statistically downscaled using a weather generator (BCC/RCG-WG) and the output of five global climate models. Based on the downscaled daily precipitation at 174 stations, eight indices describing mean and extreme precipitation climates were calculated. Overall increasing trends in the frequency and intensity of the mean and extreme precipitation were identified for the majority of the stations studied, which is in line with the GCMs’ output. However, the downscaling approach enables more local features to be reflected, adding value to applications at the local scale. Compared with the baseline during 1961–2005, the regional average annual precipitation and its intensity are projected to increase in all three future periods under both RCP 4.5 and RCP 8.5. The projected changes in the number of days with precipitation are relatively small across the Beijing-Tianjin-Hebei region. The regional average annual number of days with precipitation would increase by 0.2~1.0% under both RCP 4.5 and RCP 8.5, except during 2031–2050 under RCP 8.5 when it would decrease by 0.7%. The regional averages of annual days with precipitation ≥25 mm and ≥40 mm, the greatest one-day and five-day precipitation in the Beijing-Tianjin-Hebei region, are projected to increase by 8~30% during all the three periods. The number of days with daily precipitation ≥40 mm was projected to increase most significantly out of the eight indices, indicating the need to consider increased flooding risk in the future. The average annual maximum number of consecutive days without precipitation in the Beijing-Tianjin-Hebei region is projected to decrease, and the drought risk in this area is expected to decrease.

Understanding future increases in precipitation extremes in global land monsoon regions

This study investigates future changes in daily precipitation extremes and the involved physics over the global land monsoon (GM) region using climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The daily precipitation extreme is identified by the cutoff scale, measuring the extreme tail of the precipitation distribution. Compared to the historical period, multi-model results reveal a continuous increase in precipitation extremes under four scenarios, with a progressively higher fraction of precipitation exceeding the historical cutoff scale when moving into the future. The rise of the cutoff-scale by the end of the century is reduced by 57.8% in the moderate emission scenario relative to the highest scenario, underscoring the social benefit in reducing emissions. The cutoff scale sensitivity, defined by the increasing rates of the cutoff scale over the GM region to the global mean surface temperature increase, is nearly independent of the projected periods and emission scenarios, roughly 8.0% K−1 by averaging all periods and scenarios. To understand the cause of the changes, we applied a physical scaling diagnostic to decompose them into thermodynamic and dynamic contributions. We find that thermodynamics and dynamics have comparable contributions to the intensified precipitation extremes in the GM region. Changes in thermodynamic scaling contribute to a spatially uniform increase pattern, while changes in dynamic scaling dominate the regional differences in the increased precipitation extremes. Furthermore, the large inter-model spread of the projection is primarily attributed to variations of dynamic scaling among models.

­Comparison of Statistical Downscaling of Summer Daily Precipitation Through a Certain Perfect Prognosis and Bias Corrections - A Case Study Across China

Fewer perfect prognosis (PP) based statistical downscaling were applied to future projections produced by global circulation models (GCM), when compared with the method of model output statistics (MOS). This study is a trial to use a multiple variable based PP downscaling for summer daily precipitation at many sites in China and to compare with the MOS. For the PP method (denoted as ‘OGB-PP’), predictors for each site are screened from surface-level variables in ERA-Interim reanalysis by an optimal grid-box method, then the biases in predictors are corrected and fitted to generalized linear models to downscale daily precipitation. The historical and the future simulations under the medium emission scenario (often represented as ‘RCP4.5’), produced by three GCMs (CanESM2, HadGEM2-ES and GFDL-ESM2G) in the coupled model intercomparison project phase five (CMIP5) were used as the downscaling bases. The bias correction based MOS downscaling (denoted as ‘BC-MOS’) were used to compare with the OGB-PP. The OGB-PP generally produced the climatological mean of summer precipitation across China, based on both ERAI and CMIP5 historical simulations. The downscaled spatial patterns of long-term changes are diverse, depending on the different GCMs, different predictor-bias corrections, and the choices on selecting PP and MOS. The annual variations downscaled by OGB-PP have small differences among the choices of different predictor-bias corrections, but have large difference to that downscaled by BC-MOS. The future changes downscaled from each GCM are sensitive to the bias corrections on predictors. The overall change patterns in some OGB-PP results on future projections produced similar trends as those projected by other multiple-model downscaling in CMIP5, while the result of the BC-MOS on the same GCMs did not, implying that PP methods may be promising. OGB-PP produced more significant increasing/decreasing trends and larger spatial variability of trends than the BC-MOS methods did. The reason maybe that in OGB-PP the independent precipitation modeling mechanism and the freely selected grid-box predictors can give rise to more diverse outputs over different sites than that from BC-MOS, which can contribute additional local variability.

Chains of Spatial and Temporal Precipitation Occurrence Predictability Across the Continental U.S.

Both spatial and temporal information sources contribute to the predictability of precipitation occurrence at a given location. These sources, and the level of predictability they provide, are relevant to forecasting and understanding precipitation processes at different time scales. We use information theory-based measures to construct connected “chains of influence” of spatial extents and timescales of precipitation occurrence predictability across the continental U.S, based on gridded daily precipitation data. These regions can also be thought of as “footprints” or regions where precipitation states tend to be most synchronized. We compute these chains of precipitation influence for grid cells in the continental US, and study metrics regarding their lengths, extents, and curvature for different seasons. We find distinct geographic and seasonal patterns, particularly longer chain lengths during the summer that are indicative of larger spatial extents for storms. While synchronous, or instantaneous, relationships are strongest for grid cells in the same region, lagged relationships arise as chains reach areas farther from the original cell. While this study focuses on precipitation occurrence predictability given only information about precipitation, it could be extended to study spatial and temporal properties of other driving factors.

Contrasting scaling relationships of extreme precipitation and streamflow to temperature across the United States

Recent studies have reached inconsistent conclusions from scaling analysis about whether flood or extreme precipitation is more sensitive to warming climate. To explain the reasons behind the inconsistency, here we first used scaling analysis to illustrate how extreme daily precipitation and streamflow scale with daily air temperature across the Continental United States (CONUS). We found both similar and opposite scaling in extreme precipitation and streamflow. It indicates based on scaling analysis, the sensitivity of extreme streamflow to warming climate can be either similar, higher or lower to that of extreme precipitation. We further explored why there are contrasting scaling relationships in the CONUS. Generally, the similar scaling was found in regions where the timing of extreme precipitation and streamflow is correspondent, as well as with similar temporal evolution in extreme event timing and magnitude, e.g., the west coast and southern plains, implying extreme precipitation is the dominant driver of local floods. However, for regions with dissimilar scaling in extreme precipitation and streamflow (e.g., Rocky Mountains, southern plains), the characteristics of extreme streamflow show large difference to those of extreme precipitation, and the temporal evolution of extreme streamflow timing and magnitude are more correlated with factors/processes such as soil moisture and snowmelt. This study reflects that the contrasting scaling relationships of extreme precipitation and streamflow are oriented from the local hydro-climatological specifics. Using scaling analysis to compare the sensitivity of extreme precipitation and streamflow to warming climate is not suitable. Instead, we should focus more on local flood generating mechanisms or flood drivers when investigating floods in the changing climate.

Identifying and correcting biases in localized downscaling estimates of daily precipitation return values

Extreme precipitation events are a major cause of economic damage and disruption, and need to be addressed for increasing resilience to a changing climate, particularly at the local scale. Practitioners typically want to understand local changes at spatial scales much smaller than the native resolution of most Global Climate Models, for which downscaling techniques are used to translate planetary-to-regional scale change information to local scales. However, users of statistically downscaled outputs should be aware that how the observational data used to train the statistical models is constructed determines key properties of the downscaled solutions. Specifically for one such downscaling approach, when considering seasonal return values of extreme daily precipitation, we find that the Localized Constructed Analogs (LOCA) method produces a significant low bias in return values due to choices made in building the observational data set used to train LOCA. The LOCA low biases in daily extremes are consistent across event extremity, but do not degrade the overall performance of LOCA-derived changes in extreme daily precipitation. We show that the low (negative) bias in daily extremes is a function of a time-of-day adjustment applied to the training data and the manner of gridding daily precipitation data. The effects of these choices are likely to affect other downscaling methods trained with observations made in the same way. The results developed here show that efforts to improve resilience at the local level using extreme precipitation projections can benefit from using products specifically created to properly capture the statistics of extreme daily precipitation events.