Researchers use CHRS UCI precipitation climate data record dataset named PERSIANN-CDR and advanced spatial analysis tools to assess rainfall trends globally for the past three decades.
Changes in precipitation patterns are highly related to variability of atmospheric circulations, which can be influenced by a warming climate, i.e., shifts in storm tracks, leading to an increasing trend in global precipitation. Some suggest an increase in hydrologic extremes in response to a warming climate, while others propose location-specific intensification of the global hydrologic cycle where the wet regions get wetter and the dry regions get drier. At the same time, there is little evidence provided by historical observations supporting the notion that the wet gets wetter and the dry gets drier. In the absence of long-term, global precipitation observations, little can be said with confidence in terms of global precipitation trends. Our recently released high resolution global precipitation dataset Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), distributed by NOAA, provides the ability to reexamine and gain new insights about the global precipitation trends across different spatial scales. PERSIANN-CDR was developed based on the artificial neural network model named PERSIANN. PERSIANN-CDR uses brightness temperature retrievals of infrared information from geostationary Earth orbiting satellites to estimate rainfall and the monthly GPCP rain gauge observations for bias correction. This study offers new insights on global precipitation trends across different spatial scales, using the recently developed RainSphere tool, which allows for spatial analysis of historical precipitation observations using the PERSIANN-CDR dataset.
Using PERSIANN-CDR a number of conclusions about past trends and rainfall have been discovered. Globally (60oN - 60oS), we are observing 2.36% statistically significant increasing, 42.84% insignificant increasing, 4.48% significant decreasing, and 48.44% insignificant decreasing trend in the mean annual precipitation for 1983-2015 (all at 0.05 significance level). These trend percentages are, respectively, 2.95%, 49.74%, 2.93%, and 42.37% over the land, and 2.14%, 40.26%, 5.07%, and 50.71% over the ocean. Fluctuations are present in the total amount of precipitation that has fallen over land and ocean; however, no significant long-term volumetric change is observed for either case. Trends in the data give some insight into the effects of climate change on the distribution of rainfall. In general, warm temperate climate regions have decreasing trends while arid and polar climate regions have increasing trends in precipitation. The take-home message from our study using the new 33+ years of high-resolution global precipitation dataset is that there seems not to be any detectable and significant positive trends in the amount of global precipitation due to the now well-established increasing global temperature. While there are regional trends, there is no evidence of increase in precipitation at the global scale in response to the observed global warming. Perhaps, the explanation can be that satellite data used in this study limited to between 60oN to 60oS and any precipitation above and below these latitudes is unavailable.
Little dispute surrounds the observed global temperature changes over the past decades, and there is widespread agreement that a corresponding response in the global hydrologic cycle must exist. However, exactly how such a response manifests remains unsettled. Here we use a unique recently developed long-term satellite-based record to assess changes in global precipitation across spatial scales for the past 3 decades. This data record is the first long-term daily spatially consistent climate data record developed as part of a multi-year project funded by the National Oceanic and Atmospheric Administration (NOAA). A unique feature of this data record is that, unlike point-based observations, it is spatially consistent across the globe. Our results show opposing trends at different scales, highlighting the importance of spatial scale in trend analysis. Furthermore, while the increasing global temperature trend is apparent in observations, the same cannot be said for the global precipitation trend according to the high-resolution dataset PERSIANN-CDR used in this study.
Contacts (BER PM)
Multisector Dynamics Research
Earth and Environmental Systems Modeling
Center for Hydrometeorology & Remote Sensing, University of California - Irvine
This research was partially supported by the Department of Energy CERC-WET (DOE prime award # DE-IA0000018).
Nguyen, P., A. Thorstensen, S. Sorooshian, K. Hsu, A. AghaKouchak, H. Ashouri, H. Tran, and D. Braithwaite. “Global precipitation trends across spatial scales using satellite observations.” Bulletin of American Meteorological Society 99, 689-697 (2018). [DOI: 10.1175/BAMS-D-17-0065.1]
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