scholarly journals The Key Role of Heavy Precipitation Events in Climate Model Disagreements of Future Annual Precipitation Changes in California

2013 ◽  
Vol 26 (16) ◽  
pp. 5879-5896 ◽  
Author(s):  
David W. Pierce ◽  
Daniel R. Cayan ◽  
Tapash Das ◽  
Edwin P. Maurer ◽  
Norman L. Miller ◽  
...  
Keyword(s):  
Bias Correction ◽  
General Circulation ◽  
Climate Model ◽  
Daily Precipitation ◽  
Annual Precipitation ◽  
Mean Annual Precipitation ◽  
Precipitation Frequency ◽  
Model Average ◽  
Projected Change ◽  
Precipitation Events

Abstract Climate model simulations disagree on whether future precipitation will increase or decrease over California, which has impeded efforts to anticipate and adapt to human-induced climate change. This disagreement is explored in terms of daily precipitation frequency and intensity. It is found that divergent model projections of changes in the incidence of rare heavy (>60 mm day−1) daily precipitation events explain much of the model disagreement on annual time scales, yet represent only 0.3% of precipitating days and 9% of annual precipitation volume. Of the 25 downscaled model projections examined here, 21 agree that precipitation frequency will decrease by the 2060s, with a mean reduction of 6–14 days yr−1. This reduces California's mean annual precipitation by about 5.7%. Partly offsetting this, 16 of the 25 projections agree that daily precipitation intensity will increase, which accounts for a model average 5.3% increase in annual precipitation. Between these conflicting tendencies, 12 projections show drier annual conditions by the 2060s and 13 show wetter. These results are obtained from 16 global general circulation models downscaled with different combinations of dynamical methods [Weather Research and Forecasting (WRF), Regional Spectral Model (RSM), and version 3 of the Regional Climate Model (RegCM3)] and statistical methods [bias correction with spatial disaggregation (BCSD) and bias correction with constructed analogs (BCCA)], although not all downscaling methods were applied to each global model. Model disagreements in the projected change in occurrence of the heaviest precipitation days (>60 mm day−1) account for the majority of disagreement in the projected change in annual precipitation, and occur preferentially over the Sierra Nevada and Northern California. When such events are excluded, nearly twice as many projections show drier future conditions.

scholarly journals SPLINE MODELS OF CONTEMPORARY, 2030, 2060 AND 2090 CLIMATES FOR MICHOACÁN STATE, MÉXICO. IMPACTS ON THE VEGETATION

2012 ◽  
Vol 35 (4) ◽  
pp. 333
Author(s):  
Cuauhtémoc Sáenz-Romero ◽  
Gerald E. Rehfeldt ◽  
Nicholas L. Crookston ◽  
Pierre Duval ◽  
Jean Beaulieu
Keyword(s):  
General Circulation ◽  
Climate Models ◽  
Climate Model ◽  
Aridity Index ◽  
General Circulation Models ◽  
Annual Precipitation ◽  
Mean Annual Precipitation ◽  
Mean Annual Temperature ◽  
Emission Scenarios ◽  
Weather Stations

Climate data from 149 weather stations of Michoacán State, at Western México, were extracted from a spline climate model developed for México’s contemporary climate (1961-1990), and for climate projected for the decades centered in years 2030, 2060 and 2090. The model was constructed using outputs from three general circulation models (GCMs: Canadian, Hadley and Geophysical Fluid Dynamics) from two emission scenarios (A “pessimistic” and B “optimistic”). Mean annual temperature (MAT), mean annual precipitation (MAP), annual degree days > 5 °C (DD5), and annual aridity index (DD50.5/MAP) were mapped for Michoacán at an 1 km2 scale, and means were estimated averaging all weather stations. The state average in GCMs and emission scenarios point out that mean annual temperature would increase 1.4 °C by year 2030, 2.2 °C by year 2060 and 3.6 °C by year 2090; whereas annual precipitation would decrease 5.6 % by year 2030, 5.9 % by year 2060 and 7.8 % by year 2090. Climate models can be used for inferring plant-climate relationships and for developing programs to counteract global warming effects. Climate variables were estimated also at Pinus hartwegii and Pinus pseudostrobus growth locations, at Pico de Tancítaro in Central Western Michoacán and Nuevo San Juan Parangaricutiro (near Tancítaro), respectively. According to the annual aridity index values estimated for such locations, it is necessary to conduct assisted migration to match current genotypes to projected climates. This translates into an altitudinal shift of 400 to 450 m higher to match 2030 climates predicted by Canadian Model scenario A2, and 600 to 800 m to match 2060 climates.

scholarly journals Errors in climate model daily precipitation and temperature output: time invariance and implications for bias correction

2013 ◽  
Vol 17 (6) ◽  
pp. 2147-2159 ◽  
Author(s):  
E. P. Maurer ◽  
T. Das ◽  
D. R. Cayan
Keyword(s):  
Bias Correction ◽  
General Circulation ◽  
Daily Precipitation ◽  
Extreme Values ◽  
Circulation Model ◽  
The United States ◽  
Historical Period ◽  
Late 20Th Century ◽  
Precipitation And Temperature ◽  
Gcm Biases

Abstract. When correcting for biases in general circulation model (GCM) output, for example when statistically downscaling for regional and local impacts studies, a common assumption is that the GCM biases can be characterized by comparing model simulations and observations for a historical period. We demonstrate some complications in this assumption, with GCM biases varying between mean and extreme values and for different sets of historical years. Daily precipitation and maximum and minimum temperature from late 20th century simulations by four GCMs over the United States were compared to gridded observations. Using random years from the historical record we select a "base" set and a 10 yr independent "projected" set. We compare differences in biases between these sets at median and extreme percentiles. On average a base set with as few as 4 randomly-selected years is often adequate to characterize the biases in daily GCM precipitation and temperature, at both median and extreme values; 12 yr provided higher confidence that bias correction would be successful. This suggests that some of the GCM bias is time invariant. When characterizing bias with a set of consecutive years, the set must be long enough to accommodate regional low frequency variability, since the bias also exhibits this variability. Newer climate models included in the Intergovernmental Panel on Climate Change fifth assessment will allow extending this study for a longer observational period and to finer scales.

scholarly journals Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods

2007 ◽  
Vol 4 (5) ◽  
pp. 3413-3440 ◽  
Author(s):  
E. P. Maurer ◽  
H. G. Hidalgo
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Large Scale ◽  
Impact Analysis ◽  
Climate Model ◽  
Daily Precipitation ◽  
Circulation Model ◽  
Reanalysis Data ◽  
Temperature Extremes ◽  
Daily Data

Abstract. Downscaling of climate model data is essential to most impact analysis. We compare two methods of statistical downscaling to produce continuous, gridded time series of precipitation and surface air temperature at a 1/8-degree (approximately 140 km² per grid cell) resolution over the western U.S. We use NCEP/NCAR Reanalysis data from 1950–1999 as a surrogate General Circulation Model (GCM). The two methods included are constructed analogues (CA) and a bias correction and spatial downscaling (BCSD), both of which have been shown to be skillful in different settings, and BCSD has been used extensively in hydrologic impact analysis. Both methods use the coarse scale Reanalysis fields of precipitation and temperature as predictors of the corresponding fine scale fields. CA downscales daily large-scale data directly and BCSD downscales monthly data, with a random resampling technique to generate daily values. The methods produce comparable skill in producing downscaled, gridded fields of precipitation and temperatures at a monthly and seasonal level. For daily precipitation, both methods exhibit some skill in reproducing both observed wet and dry extremes and the difference between the methods is not significant, reflecting the general low skill in daily precipitation variability in the reanalysis data. For low temperature extremes, the CA method produces greater downscaling skill than BCSD for fall and winter seasons. For high temperature extremes, CA demonstrates higher skill than BCSD in summer. We find that the choice of most appropriate downscaling technique depends on the variables, seasons, and regions of interest, on the availability of daily data, and whether the day to day correspondence of weather from the GCM needs to be reproduced for some applications. The ability to produce skillful downscaled daily data depends primarily on the ability of the climate model to show daily skill.

Climate Model Biases and Modification of the Climate Change Signal by Intensity-Dependent Bias Correction

2018 ◽  
Vol 31 (16) ◽  
pp. 6591-6610 ◽  
Author(s):  
Martin Aleksandrov Ivanov ◽  
Jürg Luterbacher ◽  
Sven Kotlarski
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Daily Precipitation ◽  
Analytical Description ◽  
Analytical Theory ◽  
Climate Change Signal ◽  
Future Research ◽  
Model Biases

Climate change impact research and risk assessment require accurate estimates of the climate change signal (CCS). Raw climate model data include systematic biases that affect the CCS of high-impact variables such as daily precipitation and wind speed. This paper presents a novel, general, and extensible analytical theory of the effect of these biases on the CCS of the distribution mean and quantiles. The theory reveals that misrepresented model intensities and probability of nonzero (positive) events have the potential to distort raw model CCS estimates. We test the analytical description in a challenging application of bias correction and downscaling to daily precipitation over alpine terrain, where the output of 15 regional climate models (RCMs) is reduced to local weather stations. The theoretically predicted CCS modification well approximates the modification by the bias correction method, even for the station–RCM combinations with the largest absolute modifications. These results demonstrate that the CCS modification by bias correction is a direct consequence of removing model biases. Therefore, provided that application of intensity-dependent bias correction is scientifically appropriate, the CCS modification should be a desirable effect. The analytical theory can be used as a tool to 1) detect model biases with high potential to distort the CCS and 2) efficiently generate novel, improved CCS datasets. The latter are highly relevant for the development of appropriate climate change adaptation, mitigation, and resilience strategies. Future research needs to focus on developing process-based bias corrections that depend on simulated intensities rather than preserving the raw model CCS.

Assessing Goodness of Fit to a Gamma Distribution and Estimating Future Projection on Daily Precipitation Frequency Using Regional Climate Model Simulations over Japan with and without the Influence of Tropical Cyclones

2020 ◽  
Vol 21 (12) ◽  
pp. 2997-3010
Author(s):  
Akihiko Murata ◽  
Shun-ichi I. Watanabe ◽  
Hidetaka Sasaki ◽  
Hiroaki Kawase ◽  
Masaya Nosaka
Keyword(s):  
Tropical Cyclones ◽  
Gamma Distribution ◽  
Goodness Of Fit ◽  
Climate Model ◽  
Daily Precipitation ◽  
Regional Climate ◽  
Precipitation Data ◽  
Precipitation Frequency ◽  
Climate Model Simulations ◽  
Two Parameters

AbstractGoodness of fit in daily precipitation frequency to a gamma distribution was examined, focusing on adverse effects originating from the shortage of sampled tropical cyclones, using precipitation data with and without the influence of tropical cyclones. The data used in this study were obtained through rain gauge observations and regional climate model simulations under the RCP8.5 scenario and the present climate. An empirical cumulative distribution function (CDF), calculated from a sample of precipitation data for each location, was compared with a theoretical CDF derived from two parameters of a gamma distribution. Using these two CDFs, the root-mean-square error (RMSE) was calculated as an indicator of the goodness of fit. The RMSE exhibited a decreasing tendency when the influence of tropical cyclones was removed. This means that the empirical CDF derived from sampled precipitation more closely resembled the theoretical CDF when compared with the relationship between empirical and theoretical CDFs, including precipitation data associated with tropical cyclones. Future changes in the two parameters of the gamma distribution, without the influence of tropical cyclones, depend on regions in Japan, indicating a regional dependence on changes in the shape and scale of the CDF. The magnitude of increases in no-rain days was also dependent on regions of Japan, although the number of no-rain days increased overall. This simplified approach is useful for analyzing climate change from a broad perspective.

scholarly journals Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue

2013 ◽  
Vol 26 (6) ◽  
pp. 2137-2143 ◽  
Author(s):  
Douglas Maraun
Keyword(s):  
Regional Climate Model ◽  
Bias Correction ◽  
Climate Model ◽  
Daily Precipitation ◽  
Temporal Structure ◽  
Regional Climate ◽  
Quantile Mapping ◽  
Model Simulations ◽  
Scale Mismatch ◽  
Climate Model Simulations

Abstract Quantile mapping is routinely applied to correct biases of regional climate model simulations compared to observational data. If the observations are of similar resolution as the regional climate model, quantile mapping is a feasible approach. However, if the observations are of much higher resolution, quantile mapping also attempts to bridge this scale mismatch. Here, it is shown for daily precipitation that such quantile mapping–based downscaling is not feasible but introduces similar problems as inflation of perfect prognosis (“prog”) downscaling: the spatial and temporal structure of the corrected time series is misrepresented, the drizzle effect for area means is overcorrected, area-mean extremes are overestimated, and trends are affected. To overcome these problems, stochastic bias correction is required.

scholarly journals Climatology and Interannual Variability of Floods during the TRMM Era (1998–2013)

2020 ◽  
Vol 33 (8) ◽  
pp. 3289-3305 ◽  
Author(s):  
Yan Yan ◽  
Huan Wu ◽  
Guojun Gu ◽  
Zhijun Huang ◽  
Lorenzo Alfieri ◽  
...  
Keyword(s):  
Daily Precipitation ◽  
Southern Oscillation ◽  
Temporal Variations ◽  
Precipitation Frequency ◽  
Environment Model ◽  
Precipitation Characteristics ◽  
Precipitation Analysis ◽  
Precipitation Indices ◽  
Rain Rates ◽  
Precipitation Events

AbstractSpatial and temporal variations of global floods during the TRMM period (1998–2013) are explored by means of the outputs of the Dominant River Routing Integrated with VIC Environment model (DRIVE) driven by the precipitation rates from the TRMM Multisatellite Precipitation Analysis (TMPA). Climatological and seasonal mean features of floods including frequency (FF), duration (FD), and mean and total intensity (FI and FTI) are examined and further compared to those for a variety of precipitation indices derived from the daily TMPA rain rates. In general, floods and precipitation manifest similar spatial distributions, confirming that more precipitation (both amount and frequency) often indicates higher probability of floods. However, different flood indices can be associated with different precipitation characteristics with a highly region-dependent distribution. FF and FD tend to be more related to daily precipitation frequency globally, especially the mid- to high-end precipitation frequencies (F10, F25, F50). However, FI and FTI tend to be more associated with the mean volume/magnitude of those (extreme) daily precipitation events (Pr10 and Pr25). Nonetheless, daily precipitation intensity except the very high end one (R50) generally has a relatively weak effect on floods. The precipitation–flood relations at the 10 large regions are further examined, providing an improved understanding of precipitation-related flood-generating mechanisms in different locations. On the interannual time scale, El Niño–Southern Oscillation (ENSO) can significantly affect floods in many flood-prone zones. However, it is noted that even though the ENSO effect on floods is mostly through modulating various aspects of precipitation events, significant ENSO signals in precipitation cannot always translate to an effective, simultaneous impact on floods.

scholarly journals Bias correction of temperature and precipitation over China for RCM simulations using the QM and QDM methods

2020 ◽  
Author(s):  
Yao Tong ◽  
Xuejie Gao ◽  
Zhenyu Han ◽  
Yaqi Xu ◽  
Ying Xu ◽  
...  
Keyword(s):  
Bias Correction ◽  
General Circulation ◽  
Climate Model ◽  
Regional Climate ◽  
General Circulation Models ◽  
Grid Spacing ◽  
Quantile Mapping ◽  
Temperature And Precipitation ◽  
June July August ◽  
June July

Abstract Two different bias correction methods, the quantile mapping (QM) and quantile delta mapping (QDM), are applied to simulated daily temperature and precipitation over China from a set of 21st century regional climate model (the ICTP RegCM4) projections. The RegCM4 is driven by five different general circulation models (GCMs) under the representative concentration pathway RCP4.5 at a grid spacing of 25 km using the CORDEX East Asia domain. The focus is on mean temperature and precipitation in December–January–February (DJF) and June–July–August (JJA). The impacts of the two methods on the present day biases and future change signals are investigated. Results show that both the QM and QDM methods are effective in removing the systematic model biases during the validation period. For the future changes, the QDM preserves the temperature change signals well, in both magnitude and spatial distribution, while the QM artificially modifies the change signal by decreasing the warming and modifying the patterns of change. For precipitation, both methods preserve the change signals well but they produce greater magnitude of the projected increase, especially the QDM. We also show that the effects of bias correction are variable- and season-dependent. Our results show that different bias correction methods can affect in different way the simulated change signals, and therefore care has to be taken in carrying out the bias correction process.

scholarly journals Addressing the Issue of Systematic Errors in a Regional Climate Model

2007 ◽  
Vol 20 (5) ◽  
pp. 801-818 ◽  
Author(s):  
Vasubandhu Misra
Keyword(s):  
Regional Climate Model ◽  
Bias Correction ◽  
General Circulation ◽  
Climate Model ◽  
Regional Climate ◽  
Circulation Model ◽  
Systematic Errors ◽  
Driver Model ◽  
South American ◽  
Driving Model

Abstract A methodology is proposed in which a few prognostic variables of a regional climate model (RCM) are strongly constrained at certain wavelengths to what is prescribed from the bias-corrected atmospheric general circulation model (AGCM; driver model) integrations. The goal of this strategy is to reduce the systematic errors in a RCM that mainly arise from two sources: the lateral boundary conditions and the RCM errors. Bias correction (which essentially corrects the climatology) of the forcing from the driving model addresses the former source while constraining the solution of the RCM beyond certain relatively large wavelengths in the regional domain [also termed as scale-selective bias correction (SSBC)] addresses the latter source of systematic errors in RCM. This methodology is applied to experiments over the South American monsoon region. It is found that the combination of bias correction and SSBC on the nested variables of divergence, vorticity, and the log of surface pressure of an RCM yields a major improvement in the simulation of the regional climate variability over South America from interannual to intraseasonal time scales. The basis for such a strategy is derived from a systematic empirical approach that involved over 100 regional seasonal climate integrations.

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