How Often Does It Rain?

2006 ◽  
Vol 19 (6) ◽  
pp. 916-934 ◽  
Author(s):  
Ying Sun ◽  
Susan Solomon ◽  
Aiguo Dai ◽  
Robert W. Portmann
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Precipitation Amount ◽  
Heavy Precipitation ◽  
Global Climate Models ◽  
Annual Precipitation ◽  
Precipitation Frequency ◽  
Daily Precipitation Data ◽  
Precipitation Total ◽  
Rainy Days

Abstract Daily precipitation data from worldwide stations and gridded analyses and from 18 coupled global climate models are used to evaluate the models' performance in simulating the precipitation frequency, intensity, and the number of rainy days contributing to most (i.e., 67%) of the annual precipitation total. Although the models examined here are able to simulate the land precipitation amount well, most of them are unable to reproduce the spatial patterns of the precipitation frequency and intensity. For light precipitation (1–10 mm day−1), most models overestimate the frequency but produce patterns of the intensity that are in broad agreement with observations. In contrast, for heavy precipitation (>10 mm day−1), most models considerably underestimate the intensity but simulate the frequency relatively well. The average number of rainy days contributing to most of the annual precipitation is a simple index that captures the combined effects of precipitation frequency and intensity on the water supply. The different measures of precipitation characteristics examined in this paper reveal region-to-region differences in the observations and models of relevance for climate variability, water resources, and climate change.

scholarly journals Regional Perspective on Mechanisms for Tropical Precipitation Frequency and Intensity under Global Warming

2012 ◽  
Vol 25 (24) ◽  
pp. 8487-8501 ◽  
Author(s):  
Chao-An Chen ◽  
Chia Chou ◽  
Cheng-Ta Chen
Keyword(s):  
Global Warming ◽  
Climate Models ◽  
Global Climate ◽  
Longwave Radiation ◽  
Heavy Precipitation ◽  
Point Of View ◽  
Global Climate Models ◽  
Main Process ◽  
Precipitation Frequency ◽  
Horizontal Advection

Abstract From a global point of view, a shift toward more intense precipitation is often found in observations and global warming simulations. However, similar to changes in mean precipitation, these changes associated with precipitation characters, such as intensity and frequency, should vary with space. Based on the classification of the subregions for the tropics in Chou et al., changes in precipitation frequency and intensity and their association with changes in mean precipitation are analyzed on a regional basis in 10 coupled global climate models. Furthermore, mechanisms for these changes are also examined, via the thermodynamic and dynamic contributions. In general, the increase (decrease) of mean precipitation is mainly attributed to increases (decreases) in the frequency and intensity of almost all strengths of precipitation: that is, light to heavy precipitation. The thermodynamic contribution, which is associated with increased water vapor, is positive to both precipitation frequency and intensity, particularly for precipitation extremes, and varies little with space. On the other hand, the dynamic contribution, which is related to changes in the tropical circulation, is the main process for inducing the spatial variation of changes in precipitation frequency and intensity. Among mechanisms that induce the dynamic contribution, the rich-get-richer mechanism (the dynamic part), ocean feedback, and warm horizontal advection increase precipitation frequency and intensity, while the upped-ante mechanism, the deepening of convection, longwave radiation cooling, and cold horizontal advection tend to reduce precipitation frequency and intensity.

scholarly journals Observed Changes of Rain-Season Precipitation in China from 1960 to 2018

2021 ◽  
Vol 18 (19) ◽  
pp. 10031
Author(s):  
Yanyu Zhang ◽  
Shuying Zang ◽  
Xiangjin Shen ◽  
Gaohua Fan
Keyword(s):  
Climate Change ◽  
Global Climate ◽  
Regional Climate ◽  
Precipitation Amount ◽  
Heavy Precipitation ◽  
Central China ◽  
The Tibetan Plateau ◽  
Natural Ecosystems ◽  
Daily Precipitation Data ◽  
Rain Season

Precipitation during the main rain season is important for natural ecosystems and human activities. In this study, according to daily precipitation data from 515 weather stations in China, we analyzed the spatiotemporal variation of rain-season (May–September) precipitation in China from 1960 to 2018. The results showed that rain-season precipitation decreased over China from 1960 to 2018. Rain-season heavy (25 ≤ p < 50 mm/day) and very heavy (p ≥ 50 mm/day) precipitation showed increasing trends, while rain-season moderate (10 ≤ p < 25 mm/day) and light (0.1 ≤ p < 10 mm/day) precipitation showed decreasing trends from 1960 to 2018. The temporal changes of precipitation indicated that rain-season light and moderate precipitation displayed downward trends in China from 1980 to 2010 and rain-season heavy and very heavy precipitation showed fluctuant variation from 1960 to 2018. Changes of rain-season precipitation showed clear regional differences. Northwest China and the Tibetan Plateau showed the largest positive trends of precipitation amount and days. In contrast, negative trends were found for almost all precipitation grades in North China Plain, Northeast China, and North Central China. Changes toward drier conditions in these regions probably had a severe impact on agricultural production. In East China, Southeast China and Southwest China, heavy and very heavy precipitation had increased while light and moderate precipitation had decreased. This result implied an increasing risk of flood and mudslides in these regions. The advance in understanding of precipitation change in China will contribute to exactly predict the regional climate change under the background of global climate change.

scholarly journals California Wintertime Precipitation Bias in Regional and Global Climate Models

2010 ◽  
Vol 49 (10) ◽  
pp. 2147-2158 ◽  
Author(s):  
Peter Caldwell
Keyword(s):  
General Circulation ◽  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Regional Climate Models ◽  
General Circulation Models ◽  
Global Climate Models ◽  
Model Resolution ◽  
Grid Spacing ◽  
Precipitation Frequency

Abstract In this paper, wintertime precipitation from a variety of observational datasets, regional climate models (RCMs), and general circulation models (GCMs) is averaged over the state of California and compared. Several averaging methodologies are considered and all are found to give similar values when the model grid spacing is less than 3°. This suggests that California is a reasonable size for regional intercomparisons using modern GCMs. Results show that reanalysis-forced RCMs tend to significantly overpredict California precipitation. This appears to be due mainly to the overprediction of extreme events; RCM precipitation frequency is generally underpredicted. Overprediction is also reflected in wintertime precipitation variability, which tends to be too high for RCMs on both daily and interannual scales. Wintertime precipitation in most (but not all) GCMs is underestimated. This is in contrast to previous studies based on global blended gauge–satellite observations, which are shown here to underestimate precipitation relative to higher-resolution gauge-only datasets. Several GCMs provide reasonable daily precipitation distributions, a trait that does not seem to be tied to model resolution. The GCM daily and interannual variabilities are generally underpredicted.

Km-resolution climate models: prospects and challenges

2022 ◽  
Author(s):  
Christoph Schär
Keyword(s):  
Climate Change ◽  
Graphics Processing Units ◽  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Heavy Precipitation ◽  
Wave Drag ◽  
Global Climate Models ◽  
Moist Convection ◽  
Climate Change Projections

&lt;p&gt;Currently major efforts are underway toward refining the horizontal grid spacing of climate models to about 1 km, using both global and regional climate models. There is the well-founded hope that this increase in resolution will improve climate models, as it enables replacing the parameterizations of moist convection and gravity-wave drag by explicit treatments. Results suggest that this approach has a high potential in improving the representation of the water cycle and extreme events, and in reducing uncertainties in climate change projections. The presentation will provide examples of these developments in the areas of heavy precipitation and severe weather events over Europe. In addition, it will be argued that km-resolution is a promising approach toward constraining uncertainties in global climate change projections, due to improvements in the representation of tropical and subtropical clouds. Work in the latter area has only recently started and results are highly encouraging.&lt;/p&gt; &lt;p&gt;For a few years there have also been attempts to make km-resolution available in global climate models for decade-long simulations. Developing this approach requires a concerted effort. Key challenges include the exploitation of the next generation hardware architectures using accelerators (e.g. graphics processing units, GPUs), the development of suitable approaches to overcome the output avalanche, and the maintenance of the rapidly-developing model source codes on a number of different compute architectures. Despite these challenges, it will be argued that km-resolution GCMs with a capacity to run at 1 SYPD (simulated year per day), might be much closer than commonly believed.&lt;/p&gt; &lt;p&gt;The presentation is largely based on a recent collaborative paper (Sch&amp;#228;r et al., 2020, BAMS, https://doi.org/10.1175/BAMS-D-18-0167.1) and ongoing studies. It will also present aspects of a recent Swiss project in this area (EXCLAIM, https://exclaim.ethz.ch/).&lt;/p&gt;

scholarly journals The Influence of Climate Model Biases on Projections of Aridity and Drought

2016 ◽  
Vol 29 (4) ◽  
pp. 1269-1285 ◽  
Author(s):  
Darren L. Ficklin ◽  
John T. Abatzoglou ◽  
Scott M. Robeson ◽  
Anna Dufficy
Keyword(s):  
United States ◽  
Climate Models ◽  
Climate Model ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Precipitation Amount ◽  
Severity Index ◽  
Global Climate Models ◽  
Drought Severity ◽  
Climate Conditions

Abstract Global climate models (GCMs) have biases when simulating historical climate conditions, which in turn have implications for estimating the hydrological impacts of climate change. This study examines the differences in projected changes of aridity [defined as the ratio of precipitation (P) over potential evapotranspiration (PET), or P/PET] and the Palmer drought severity index (PDSI) between raw and bias-corrected GCM output for the continental United States (CONUS). For historical simulations (1950–79) the raw GCM ensemble median has a positive precipitation bias (+24%) and negative PET bias (−7%) compared to the bias-corrected output when averaged over CONUS with the most acute biases over the interior western United States. While both raw and bias-corrected GCM ensembles project more aridity (lower P/PET) for CONUS in the late twenty-first century (2070–99), relative enhancements in aridity were found for bias-corrected data compared to the raw GCM ensemble owing to positive precipitation and negative PET biases in the raw GCM ensemble. However, the bias-corrected GCM ensemble projects less acute decreases in summer PDSI for the southwestern United States compared to the raw GCM ensemble (from 1 to 2 PDSI units higher), stemming from biases in precipitation amount and seasonality in the raw GCM ensemble. Compared to the raw GCM ensemble, bias-corrected GCM inputs not only correct for systematic errors but also can produce high-resolution projections that are useful for impact analyses. Therefore, changes in hydroclimate metrics often appear considerably different in bias-corrected output compared to raw GCM output.

scholarly journals Observations and climate models confirm precipitation pattern is changing over Nepal

Jalawaayu ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 25-46
Author(s):  
Rocky Talchabhadel
Keyword(s):  
Climate Models ◽  
Daily Precipitation ◽  
Precipitation Amount ◽  
Coupled Model ◽  
Heavy Precipitation ◽  
Precipitation Pattern ◽  
Daily Precipitation Data ◽  
Comprehensive Picture ◽  
Intercomparison Project ◽  
Precipitation Indices

This paper presents a comprehensive picture of precipitation variability across Nepal over the present (1985-2014) and future (2021-2050) based on gauge-based observations from 28 precipitation stations distributed throughout the country and thirteen climate models of the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socioeconomic Pathways (SSP 245 and SSP 585). Seventeen different precipitation indices are computed using daily precipitation data based on gauge-based observations and climate models. Along with absolute extreme precipitation indices, such as maximum 1-day, maximum consecutive 3-day, 5-day, and 7-day precipitation amounts, this study also computes the contribution of such instances to the annual precipitation. The selected precipitation indices not only allow for the analyses of heavy precipitation-related extremes but also guide the evaluation of agricultural productivity and drought indications, such as consecutive dry and wet days (CDD and CWD). The number of wet days and average precipitation during those wet days, along with the information of the number of days with daily precipitation ≥ 10, 20, 50, and 100 mm, summarize the distribution of total precipitation. This study emphasizes changing precipitation patterns by looking at these indices over the present and future periods. Observations and climate models show a changing nature of precipitation over Nepal. However, different climate models exhibit a different severity of changes. Though the yearly precipitation amount is not altered noticeably, this study finds that the extremes are expected to alter significantly than the averages. It is also to be noted that climate models are unable to capture localized extremes in Nepal Himalayas.

scholarly journals Predicted Changes in the Frequency of Extreme Precipitable Water Vapor Events

2015 ◽  
Vol 28 (18) ◽  
pp. 7057-7070 ◽  
Author(s):  
Jacola Roman ◽  
Robert Knuteson ◽  
Steve Ackerman ◽  
Hank Revercomb
Keyword(s):  
United States ◽  
Water Vapor ◽  
Climate Models ◽  
Global Climate ◽  
Precipitable Water ◽  
Heavy Precipitation ◽  
Precipitable Water Vapor ◽  
Global Climate Models ◽  
Eastern United States ◽  
Mean Trend

Abstract A high amount of precipitable water vapor (PWV) is a necessary requirement for heavy precipitation and extreme flooding events. This study determined the predicted shift in extreme PWV from a set of CMIP5 global climate models using the highest emission scenario over three different spatial resolutions (global, zonal, and regional) and four different case regions (India, China, Europe, and eastern United States). For the globe, the frequency of the extreme 1% of PWV events between 2006 and 2030 was predicted to increase by a median factor (herein called an X factor) of 9 by 2075–99. Areas of high PWV, like the tropics, tended toward higher factors. The annual median X factor for India, China, central Europe, and the eastern United States was 24, 17, 15, and 16, respectively. For India, the minimum median X factor was 10 during December–February (DJF) and the maximum was 48 during June–August (JJA). In China, the minimum median X factor (8) occurred during DJF, and the maximum was 42 in JJA. For Europe, DJF and September–November (SON) had the smallest median X factor of 15, whereas JJA had the largest median X factor of 30. The smallest median X factor for the eastern United States (11) occurred during March–May (MAM), whereas the largest median X factor (32) occurred in JJA. Regional X factors were significantly larger than global (1.5–2 times larger), illustrating the importance of regional assessments of extreme PWV. The mean trend in the extreme PWV was approximately linear for all regions with a slope of about 3% decade−1. Observations for 10 (20) years are needed for the extreme PWV to change by an amount that exceeds a 3% (5%) measurement error.

scholarly journals Increased Interannual Precipitation Extremes over California under Climate Change

2015 ◽  
Vol 28 (16) ◽  
pp. 6324-6334 ◽  
Author(s):  
Neil Berg ◽  
Alex Hall
Keyword(s):  
Interannual Variability ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
First Century ◽  
Precipitation Extremes ◽  
Wet Season ◽  
Precipitation Total ◽  
The Mean ◽  
Projected Changes

Abstract Changes to mean and extreme wet season precipitation over California on interannual time scales are analyzed using twenty-first-century precipitation data from 34 global climate models. Models disagree on the sign of projected changes in mean precipitation, although in most models the change is very small compared to historical and simulated levels of interannual variability. For the 2020/21–2059/60 period, there is no projected increase in the frequency of extremely dry wet seasons in the ensemble mean. Wet extremes are found to increase to around 2 times the historical frequency, which is statistically significant at the 95% level. Stronger signals emerge in the 2060/61–2099/2100 period. Across all models, extremely dry wet seasons are roughly 1.5 to 2 times more common, and wet extremes generally triple in their historical frequency (statistically significant). Large increases in precipitation variability in most models account for the modest increases to dry extremes. Increases in the frequency of wet extremes can be ascribed to equal contributions from increased variability and increases to the mean. These increases in the frequency of interannual precipitation extremes will create severe water management problems in a region where coping with large interannual variability in precipitation is already a challenge. Evidence from models and observations is examined to understand the causes of the low precipitation associated with the 2013/14 drought in California. These lines of evidence all strongly indicate that the low 2013/14 wet season precipitation total can be very likely attributed to natural variability, in spite of the projected future changes in extremes.

scholarly journals What rainfall rates are most important to wet removal of different aerosol types?

2021 ◽  
Vol 21 (22) ◽  
pp. 16797-16816
Author(s):  
Yong Wang ◽  
Wenwen Xia ◽  
Guang J. Zhang
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Deep Convection ◽  
Global Climate Models ◽  
Precipitation Frequency ◽  
Light Rain ◽  
Community Atmosphere Model ◽  
Novel Approach ◽  
Aerosol Types ◽  
Wet Removal

Abstract. Both frequency and intensity of rainfall affect aerosol wet deposition. With a stochastic deep convection scheme implemented into two state-of-the-art global climate models (GCMs), a recent study found that aerosol burdens are increased globally by reduced climatological mean wet removal of aerosols due to suppressed light rain. Motivated by their work, a novel approach is developed in this study to detect what rainfall rates are most efficient for wet removal (scavenging amount mode) of different aerosol species of different sizes in GCMs and applied to the National Center for Atmospheric Research Community Atmosphere Model version 5 (CAM5) with and without the stochastic convection cases. Results show that in the standard CAM5, no obvious differences in the scavenging amount mode are found among different aerosol types. However, the scavenging amount modes differ in the Aitken, accumulation and coarse modes, showing around 10–12, 8–9 and 7–8 mm d−1, respectively, over the tropics. As latitude increases poleward, the scavenging amount mode in each aerosol mode is decreased substantially. The scavenging amount mode is generally smaller over land than over ocean. With stochastic convection, the scavenging amount mode for all aerosol species in each mode is systematically increased, which is the most prominent along the Intertropical Convergence Zone, exceeding 20 mm d−1 for small particles. The scavenging amount modes in the two cases are both smaller than individual rainfall rates associated with the most accumulated rain (rainfall amount mode), further implying precipitation frequency is more important than precipitation intensity for aerosol wet removal. The notion of the scavenging amount mode can be applied to other GCMs to better understand the relation between rainfall and aerosol wet scavenging, which is important to better simulate aerosols.

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