Precipitation

About: Precipitation is a research topic. Over the lifetime, 32861 publications have been published within this topic receiving 990496 citations. The topic is also known as: rain & rainfall.

Benefit of convection permitting climate model simulations in the representation of convective precipitation

Abstract: A major source of uncertainty in regional climate model (RCM) simulations arises from the parameterisation of sub-grid scale convection. With increasing model resolution, approaching the so-called convection permitting scale, it is possible to switch off most of the convection parameterisations. A set of simulations using COSMO-CLM model has been carried out at different resolutions in order to investigate possible improvements and limitations resulting from increased horizontal resolution. For our analysis, 30 years were simulated in a triple nesting setup with 50, 7 and 2.8 km resolutions, with ERA40 reanalysis data at the lateral boundaries of the coarsest nest. The investigation area covers the state of Baden-Wurttemberg in southwestern Germany, which is a region known for abundant orographically induced convective precipitation. A very dense network of high temporal resolution rain gauges is used for evaluation of the model simulations. The purpose of this study is to examine the differences between the 7 and 2.8 km resolutions in the representation of precipitation at sub-daily timescales, and the atmospheric conditions leading to convection. Our results show that the highest resolution of RCM simulations significantly improves the representation of both hourly intensity distribution and diurnal cycle of precipitation. In addition, at convection permitting scale the atmospheric fields related to convective precipitation show a better agreement with each other. The results imply that higher spatial resolution partially improves the representation of the precipitation field, which must be the way forward for regional climate modelling.

Climate change impacts on the hydrology of a snowmelt driven basin in semiarid Chile

Abstract: In this paper we present an analysis of the direct impacts of climate change on the hydrology of the upper watersheds (range in elevation from 1,000 to 5,500 m above sea level) of the snowmelt-driven Limari river basin, located in north-central Chile (30° S, 70° W). A climate-driven hydrology and water resources model was calibrated using meteorological and streamflow observations and later forced by a baseline and two climate change projections (A2, B2) that show an increase in temperature of about 3–4°C and a reduction in precipitation of 10–30% with respect to baseline. The results show that annual mean streamflow decreases more than the projected rainfall decrease because a warmer climate also enhances water losses to evapotranspiration. Also in future climate, the seasonal maximum streamflow tends to occur earlier than in current conditions, because of the increase in temperature during spring/summer and the lower snow accumulation in winter.

Analysis of daily variability of precipitation in a nested regional climate model : comparison with observations and doubled CO2 results, Global Planet

Abstract: Abstract We analyze daily mean, variability, and frequency of precipitation in two continuous 3 1 2 year long climate simulations over the continental U.S., one for present conditions and one for conditions under doubled carbon dioxide concentration, conducted with a regional climate model (RegCM) nested in a general circulation model (GCM). The purpose of the work is to analyze model errors and limitations in greater detail than previously done and to calculate quantities that eventually will be used to form climate change scenarios that account for changes in daily variability of precipitation. The models used are a version of the NCAR Community Climate Model (CCM) and the climate version (RegCM) of the NCAR/Penn State mesoscale model (MM4) at 60 km horizontal grid point spacing. Model output is compared with a 30-year daily observational data set for mainly two regions of the U.S.: the Northwest, and the central Great Plains. Statistics compared include mean daily precipitation, mean daily intensity, frequency, transition probabilities, quantiles of precipitation intensity, and interquartile ranges. We discuss how different measures of daily precipitation lead to different conclusions about the quality of the control run. For example, good agreement between model and observed data regarding mean daily precipitation usually results from compensating errors in the intensity and frequency fields (too high frequency and too low intensity). We analyze how detailed topographic features of the RegCM enhance the simulation of daily precipitation compared to the CCM simulation. In general, errors in all measures are smallest at the Northwest grid points, and the damping of the seasonal cycle of mean daily precipitation from the coast to inland Oregon is basically well reproduced. However, some errors in the frequency and intensity fields can be traced to inadequate representation of topography, even with a horizontal resolution of 60 km. Differences in the control and doubled CO2 runs (for both RegCM and CCM) for these regions are also presented. The most significant changes for the RegCM grid points is increased variability of daily precipitation under doubled CO2 conditions. Areas with significant changes (both increases and decreases) of precipitation frequency and intensity are found. There are some areas where frequency decreases, but precipitation mean daily amounts increase. Such changes, which would be masked by more traditional analyses of precipitation change, are important from a climate impacts point of view. The limitations on the analyses posed by small sample sizes are discussed.

Intra-seasonal precipitation patterns and above-ground productivity in three perennial grasslands

Abstract: Summary 1 Relationships between above-ground net primary productivity (ANPP) of grasslands and annual precipitation are often weak at the site level, with much of the inter-annual variation in ANPP left unexplained. A potential reason for this is that the distribution of precipitation within a growing season affects productivity in addition to the total amount. 2 We analysed long-term ANPP data for three southern African temperate grasslands (mean annual precipitation ranging from 538 mm to 798 mm) to determine the effects of precipitation event size, number and spacing relative to seasonal totals. 3 Ungrazed, non-manipulated treatments at each site showed contrasting results despite sharing a common, dominant species. At the driest site, a model combining average event size and number of events per growing season provided a substantially better fit to the ANPP data than precipitation amount (seasonal total). At the wettest site, the interval between events was the most important precipitation variable. Precipitation distribution was not important at the intermediate site where amount was the best predictor of ANPP. A limit to the size of precipitation events efficiently utilized for ANPP was evident for the driest site only. 4 At each site, experimental treatments that altered species composition and soil fertility had little effect on precipitation–ANPP relationships. The lack of consistency in the relative importance of the precipitation variables among sites suggests that local, edaphic factors modify precipitation–ANPP relationships. 5 This analysis demonstrates that the distribution and size of precipitation events can affect ANPP independent of precipitation amount. As altered precipitation regimes are forecast by global climate models, the sensitivity of ecosystems to precipitation distribution should be considered when predicting responses to climate change. 6 While mean values of precipitation, and other ecosystem drivers, are typically used to predict function at the level of whole ecosystems, our results show that more complex measures of environmental variability may be required to understand ecosystem function, and to increase the accuracy of predictions of ecosystem responses to global change.