Limited Skill of Projected Land Precipitation by IPCC Models During 2002−2020
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Abstract Climate models are widely used to project future climate changes and analyze underlying mechanisms. However, it remains unknown whether the earlier precipitation projections match the subsequent observations in recent decades, which is the scientific basis for the confidence of precipitation projections and associated impacts. Here, we find models in the Third (TAR), Fourth (AR4), and Fifth (AR5) Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC) skillfully project the subsequent climatological changes in global mean land precipitation for 2002–2020, 2008–2020, and 2014–2020 (relative to 1980–1999 climatology) from several to 10 years ahead, respectively. Skillful regional projections are mainly found in the northern mid to high latitudes. IPCC models are less skillful in projecting subsequent changes in land precipitation at regional scales than at global scales, which is likely to be at least partly explained by the lower signal‐to‐noise ratio.Decision‐scale relevant climate information on climate change is needed to inform policy and decision making but often involves high uncertainty. To enhance confidence in interpreting regional climate projections, it is important to understand the underlying physical processes driving the change. This study explores a methodology to investigate climate change as a function of changes in frequency of synoptic circulation. The approach examines how dynamically downscaled future climate from two regional climate models (RCMs) from the Coordinated Regional Climate Downscaling Experiment (CORDEX), driven with four general circulation models (GCMs), can give rise to surface climate changes that differ from those of the driving GCMs. The study focuses on changes in precipitation and the circulation processes driving the projected changes from the regional climate simulations. Despite uncertainty in future projections, the RCMs and GCMs both show decreases in precipitation over most of southern Africa and suggest a reduction (increase) in the frequency of circulation patterns associated with precipitation (no precipitation) over the region. However, some contradictions are seen in the centre of the domain for some ensemble members. This study shows that some of this disagreement in precipitation projections between GCMs and RCMs is due to the inconsistencies in the physical parameterizations of precipitation processes rather than inconsistences in regional‐scale circulation patterns.
Atmospheric Circulation
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Abstract An approach for downscaling daily precipitation extremes using historical analogs is applied to simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5). The method employs a multistep procedure in which the occurrence of extreme precipitation on a given target day is determined on the basis of the probability of extreme precipitation on that day’s closest historical analogs. If extreme precipitation is expected, daily precipitation observations associated with the historical analogs are used to approximate precipitation amounts on the target day. By applying the analog method to historical simulations, the ability of the CMIP5 models to simulate synoptic weather patterns associated with extreme precipitation is assessed. Differences between downscaled and observed precipitation extremes are investigated by comparing the precipitation frequency distributions for a subset of rarely selected extreme analog days with those for all observed days with extreme precipitation. A supplemental composite analysis of the synoptic weather patterns on these rarely selected analog days is utilized to elucidate the meteorological factors that contribute to such discrepancies. Overall, the analog method as applied to CMIP5 simulations yields realistic estimates of historical precipitation extremes, with return-period precipitation biases that are comparable in magnitude to those obtained from dynamically downscaled simulations. The analysis of rarely selected analog days reveals that precipitation amounts on these days are generally larger than precipitation amounts on all days with extreme precipitation, leading to an underestimation of return-period precipitation amounts at many stations. Furthermore, the synoptic composite analysis reveals that tropical cyclones are a common feature on these rarely selected analog days.
Quantitative precipitation forecast
Quantitative precipitation estimation
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Quantile
East Asian Monsoon
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This study examines potential future changes of precipitation in China based on Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model projections for the medium (RCP4.5) and high (RCP8.5) emission scenarios. We first evaluate the biases of climate model output and correct the biases through quantile mapping. After bias correction, we examine the changes in mean precipitation as well as shifts in its frequency distribution. We also evaluate the changes in extreme precipitation based on frequency analysis techniques. Our results show that by the end of the century, mean precipitation is going to increase by 8% (12%) under RCP4.5 (RCP8.5) scenarios, resulted from a combination of an increase in precipitation intensity and a slight decrease in precipitation frequency. Spatially, precipitation is projected to increase more in northern China than southern China, and the increase is the least in the southeast. Seasonally, precipitation is projected to increase more in fall and winter, and less in spring and summer. The precipitation intensity distribution is likely to shift towards more heavy events, with a decrease in the contribution from light events and a significant increase in contribution from heavy events. Extreme precipitation is going to increase at much higher rates than mean precipitation, and the increase is more spatially uniform. Changes in annual and seasonal precipitation are closely linked with temperature change. Total precipitation increases at 2.6% (1.9%) per degree warming under RCP4.5 (RCP8.5), but extreme precipitation has much higher sensitivities ranging 4.5–6.5% per degree warming for events of various return intervals. The percentage increase per degree is generally smaller for RCP8.5 than RCP4.5 scenarios, suggesting a reduced sensitivity at higher temperature. In addition, the precipitation increase seems to be linked with changes in the atmospheric circulations that transport moisture in different regions in China. These changes have significant implications for the management of water resources and water‐related hazards.
Quantile
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Given the availability of a new generation of general circulation model simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive, we attempt to evaluate the model output by using three variants of the transformed Mielke measure to assess (1) the performance of the models in simulating historical surface temperature and precipitation, (2) the climate change response of the models to future greenhouse gases (GHGs) scenarios, and (3) the consistency of the projected change of each model with that of the multi-model ensemble (MME) mean. Most models exhibited varying degrees of skills, depending on the region and season, whereas a few models were identified as performing well globally, including the CMCC models, IPSL-CM5A-MR, and BCC-CSM1.1M. Models with the highest and lowest climate sensitivities, as well as those that project future climate changes most resembling the MME mean, were identified. The future precipitation and temperature changes projected by the MPI models and NCAR-CESM1 models were found to best resemble the overall MME. Finer resolution was found to improve model performance in simulating historical climate in most regions and seasons, particularly for temperature; however, it does not have a significant effect on the response of model climates to future GHGs scenarios. We found that no model can simultaneously exhibits good performance in simulating historical climate and in projecting a future climate that is close to the MME mean. Determining the 'best' overall model is difficult because 'best' is dependent on the specific applications for which a model will be used. Evaluating climate models is an important step to build confidence in their application for impact assessment. Our study provides a basis for concerned groups choosing climate models for their subsequent studies.
Transient climate simulation
Mean radiant temperature
Surface air temperature
Representative Concentration Pathways
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We evaluated the precipitation climatology of the Intermountain Region (IR) as generated by the six regional climate models of the North American Regional Climate Change Assessment Program (NARCCAP). A complex combination of the precipitation annual and semiannual cycles with their different phases form four major climate regimes over the IR. Each model produces systematic biases in the central IR where these different climate regimes meet. The simulated annual cycles are universally too strong, and the winter precipitation is too large. On the other hand, the semiannual cycles are relatively well produced. The strong annual cycles and the excess winter precipitation obscure the signals of spring/summer precipitation and may have led to false signals of the El Niño‐Southern Oscillation (ENSO) found in the central IR. Therefore, caution is advised when interpreting the simulated NARCCAP precipitation for the IR.
Annual cycle
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