scholarly journals How Well Do CMIP6 Historical Runs Match Observed Northeast U.S. Precipitation and Extreme Precipitation–Related Circulation?

2020 ◽  
Vol 33 (22) ◽  
pp. 9835-9848 ◽  
Author(s):  
Laurie Agel ◽  
Mathew Barlow

AbstractSixteen historical simulations (1950–2014) from phase 6 of the Coupled Model Intercomparison Project (CMIP6) are compared to Northeast U.S. observed precipitation and extreme precipitation–related synoptic circulation. A set of metrics based on the regional climate is used to assess how realistically the models simulate the observed distribution and seasonality of extreme precipitation, as well as the synoptic patterns associated with extreme precipitation. These patterns are determined by k-means typing of 500-hPa geopotential heights on extreme precipitation days (top 1% of days with precipitation). The metrics are formulated to evaluate the models’ extreme precipitation spatial variations, seasonal frequency, and intensity; and for circulation, the fit to observed patterns, pattern seasonality, and pattern location of extreme precipitation. Based on the metrics, the models vary considerably in their ability to simulate different aspects of regional precipitation, and a realistic simulation of the seasonality and distribution of precipitation does not necessarily correspond to a realistic simulation of the circulation patterns (reflecting the underlying dynamics of the precipitation), and vice versa. This highlights the importance of assessing both precipitation and its associated circulation. While the models vary in their ability to reproduce observed results, in general the higher-resolution models score higher in terms of the metrics. Most models produce more frequent precipitation than that for observations, but capture the seasonality of precipitation intensity well, and capture at least several of the key characteristics of extreme precipitation–related circulation. These results do not appear to reflect a substantial improvement over a similar analysis of selected CMIP5 models.

2020 ◽  
Vol 33 (22) ◽  
pp. 9817-9834
Author(s):  
Laurie Agel ◽  
Mathew Barlow ◽  
Joseph Polonia ◽  
David Coe

AbstractHistorical simulations from 14 models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) are evaluated for their ability to reproduce observed precipitation in the northeastern United States and its associated circulation, with particular emphasis on extreme (top 1%) precipitation. The models are compared to observations in terms of the spatial variations of extreme precipitation, seasonal cycles of precipitation and extreme precipitation frequency and intensity, and extreme precipitation circulation regimes. The circulation regimes are identified using k-means clustering of 500-hPa geopotential heights on extreme precipitation days, in both observations and in the models. While all models capture an observed northwest-to-southeast gradient of precipitation intensity (reflected in the top 1% threshold), there are substantial differences from observations in the magnitude of the gradient. These differences tend to be more substantial for lower-resolution models. However, regardless of resolution, and despite a bias toward too-frequent precipitation, many of the models capture the seasonality of observed daily precipitation intensity, and the approximate magnitude and seasonality of observed extreme precipitation intensity. Many of the simulated extreme precipitation circulation patterns are visually similar to the set of observed patterns. However, the location and magnitude of specific troughs and ridges within the patterns, as well as the seasonality of the patterns, may differ substantially from the observed corresponding patterns. A series of metrics is developed based on the observed regional characteristics to facilitate comparison between models.


2016 ◽  
Vol 17 (11) ◽  
pp. 2785-2797 ◽  
Author(s):  
Yanjuan Wu ◽  
Shuang-Ye Wu ◽  
Jiahong Wen ◽  
Felipe Tagle ◽  
Ming Xu ◽  
...  

Abstract In this study, the potential future changes of mean and extreme precipitation in the middle and lower Yangtze River basin (MLYRB), eastern China, are assessed using the models of phase 5 of the Coupled Model Intercomparison Project (CMIP5). Historical model simulations are first compared with observations in order to evaluate model performance. In general, the models simulate the precipitation mean and frequency better than the precipitation intensity and extremes, but still have difficulty capturing precipitation patterns over complex terrains. They tend to overestimate precipitation mean, frequency, and intensity while underestimating the extremes. After correcting for model biases, the spatial variation of mean precipitation projected by the multimodel ensemble mean (MME) is improved, so the MME after the bias correction is used to project changes for the years 2021–50 and 2071–2100 relative to 1971–2000 under two emission scenarios: RCP4.5 and RCP8.5. Results show that with global warming, precipitation will become less frequent but more intense over the MLYRB. Relative changes in extremes generally exceed those in mean precipitation. Moreover, increased precipitation extremes are also expected even in places where mean precipitation is projected to decrease in 2021–50. The overall increase in extreme precipitation could potentially lead to more frequent floods in this already flood-prone region.


2015 ◽  
Vol 56 (70) ◽  
pp. 89-97 ◽  
Author(s):  
Marion Réveillet ◽  
Antoine Rabatel ◽  
Fabien Gillet-Chaulet ◽  
Alvaro Soruco

AbstractBolivian glaciers are an essential source of fresh water for the Altiplano, and any changes they may undergo in the near future due to ongoing climate change are of particular concern. Glaciar Zongo, Bolivia, located near the administrative capital La Paz, has been extensively monitored by the GLACIOCLIM observatory in the last two decades. Here we model the glacier dynamics using the 3-D full-Stokes model Elmer/Ice. The model was calibrated and validated over a recent period (1997–2010) using four independent datasets: available observations of surface velocities and surface mass balance were used for calibration, and changes in surface elevation and retreat of the glacier front were used for validation. Over the validation period, model outputs are in good agreement with observations (differences less than a small percentage). The future surface mass balance is assumed to depend on the equilibrium-line altitude (ELA) and temperature changes through the sensitivity of ELA to temperature. The model was then forced for the 21st century using temperature changes projected by nine Coupled Model Intercomparison Project phase 5 (CMIP5) models. Here we give results for three different representative concentration pathways (RCPs). The intermediate scenario RCP6.0 led to 69 ± 7% volume loss by 2100, while the two extreme scenarios, RCP2.6 and RCP8.5, led to 40 ± 7% and 89 ± 4% loss of volume, respectively.


Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 675 ◽  
Author(s):  
Almazroui

This paper investigates the temperature and precipitation extremes over the Arabian Peninsula using data from the regional climate model RegCM4 forced by three Coupled Model Intercomparison Project Phase 5 (CMIP5) models and ERA–Interim reanalysis data. Indices of extremes are calculated using daily temperature and precipitation data at 27 meteorological stations located across Saudi Arabia in line with the suggested procedure from the Expert Team on Climate Change Detection and Indices (ETCCDI) for the present climate (1986–2005) using 1981–2000 as the reference period. The results show that RegCM4 accurately captures the main features of temperature extremes found in surface observations. The results also show that RegCM4 with the CLM land–surface scheme performs better in the simulation of precipitation and minimum temperature, while the BATS scheme is better than CLM in simulating maximum temperature. Among the three CMIP5 models, the two best performing models are found to accurately reproduce the observations in calculating the extreme indices, while the other is not so successful. The reason for the good performance by these two models is that they successfully capture the circulation patterns and the humidity fields, which in turn influence the temperature and precipitation patterns that determine the extremes over the study region.


2017 ◽  
Author(s):  
Richard Wartenburger ◽  
Martin Hirschi ◽  
Markus G. Donat ◽  
Peter Greve ◽  
Andy J. Pitman ◽  
...  

Abstract. This article extends a previous study (Seneviratne et al., 2016) to provide regional analyses of changes in climate extremes as a function of projected changes in global mean temperature. We introduce the DROUGHT-HEAT Regional Climate Atlas, an interactive tool to analyse and display a range of well-established climate extremes and water-cycle indices and their changes as a function of global warming. These projections are based on simulations from the 5th phase of the Coupled Model Intercomparison Project (CMIP5). A selection of example results are presented here, but users can visualize specific indices of interest using the online tool. This implementation enables a direct assessment of regional climate changes associated with global temperature targets, such as the 2 degree and 1.5 degree limits agreed within the 2015 Paris Agreement.


2013 ◽  
Vol 6 (5) ◽  
pp. 1705-1714 ◽  
Author(s):  
J. Xu ◽  
L. Zhao ◽  

Abstract. On the basis of the fifth Coupled Model Intercomparison Project (CMIP5) and the climate model simulations covering 1979 through 2005, the temperature trends and their uncertainties have been examined to note the similarities or differences compared to the radiosonde observations, reanalyses and the third Coupled Model Intercomparison Project (CMIP3) simulations. The results show noticeable discrepancies for the estimated temperature trends in the four data groups (radiosonde, reanalysis, CMIP3 and CMIP5), although similarities can be observed. Compared to the CMIP3 model simulations, the simulations in some of the CMIP5 models were improved. The CMIP5 models displayed a negative temperature trend in the stratosphere closer to the strong negative trend seen in the observations. However, the positive tropospheric trend in the tropics is overestimated by the CMIP5 models relative to CMIP3 models. While some of the models produce temperature trend patterns more highly correlated with the observed patterns in CMIP5, the other models (such as CCSM4 and IPSL_CM5A-LR) exhibit the reverse tendency. The CMIP5 temperature trend uncertainty was significantly reduced in most areas, especially in the Arctic and Antarctic stratosphere, compared to the CMIP3 simulations. Similar to the CMIP3, the CMIP5 simulations overestimated the tropospheric warming in the tropics and Southern Hemisphere and underestimated the stratospheric cooling. The crossover point where tropospheric warming changes into stratospheric cooling occurred near 100 hPa in the tropics, which is higher than in the radiosonde and reanalysis data. The result is likely related to the overestimation of convective activity over the tropical areas in both the CMIP3 and CMIP5 models. Generally, for the temperature trend estimates associated with the numerical models including the reanalyses and global climate models, the uncertainty in the stratosphere is much larger than that in the troposphere, and the uncertainty in the Antarctic is the largest. In addition, note that the reanalyses show the largest uncertainty in the lower tropical stratosphere, and the CMIP3 simulations show the largest uncertainty in both the south and north polar regions.


2013 ◽  
Vol 26 (19) ◽  
pp. 7692-7707 ◽  
Author(s):  
Yao Yao ◽  
Yong Luo ◽  
Jianbin Huang ◽  
Zongci Zhao

Abstract The extreme monthly-mean temperatures simulated by 28 models in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are evaluated and compared with those from 24 models in the third phase of the Coupled Model Intercomparison Project (CMIP3). Comparisons with observations and reanalyses indicate that the models from both CMIP3 and CMIP5 perform well in simulating temperature extremes, which are expressed as 20-yr return values. When the climatological annual cycle is removed, the ensemble spread in CMIP5 is smaller than that in CMIP3. Benefitting from a higher resolution, the CMIP5 models perform better at simulating extreme temperatures on the local gridcell scale. The CMIP5 representative concentration pathway (RCP4.5) and CMIP3 B1 experiments project a similar change pattern in the near future for both warm and cold extremes, and the pattern is in agreement with that of the seasonal extremes. By the late twenty-first century, the changes in monthly temperature extremes projected under the three CMIP3 (B1, A1B, and A2) and two CMIP5 (RCP4.5 and RCP8.5) scenarios generally follow the changes in climatological annual cycles, which is consistent with previous studies on daily extremes. Compared with the CMIP3 ensemble, the CMIP5 ensemble shows a larger intermodel uncertainty with regard to the change in cold extremes in snow-covered regions. Enhanced changes in extreme temperatures that exceed the global mean warming are found in regions where the retreat of snow (or the soil moisture feedback effect) plays an important role, confirming the findings for daily temperature extremes.


2020 ◽  
Author(s):  
June-Yi Lee ◽  
Kyung-Sook Yun ◽  
Arjun Babu ◽  
Young-Min Yang ◽  
Eui-Seok Chung ◽  
...  

<p><span>The Coupled Model Intercomparison Project Phase 5 (CMIP5) models have showed substantial inter-model spread in estimating annual global-mean precipitation change per one-degree greenhouse-gas-induced warming (precipitation sensitivity), ranging from -4.5</span><span>–4.2</span><span>%</span><sup><span>o</span></sup><span>C<sup>-1</sup>in the Representative Concentration Pathway (RCP) 2.6, the lowest emission scenario, to 0.2–4.0</span><span>%</span><sup><span>o</span></sup><span>C<sup>-1</sup>in the RCP 8.5, the highest emission scenario. The observed-based estimations in the global-mean land precipitation sensitivity during last few decades even show much larger spread due to the considerable natural interdecadal variability, role of anthropogenic aerosol forcing, and uncertainties in observation. This study tackles to better quantify and constrain global land precipitation change in response to global warming by analyzing the new range of Shared Socio-economic Pathway (SSP) scenarios in the </span><span>Coupled Model Intercomparison Project Phase 6 (CMIP6) compared with RCP scenarios in the CMIP5. We show that the range of projected change in annual global-mean land (ocean) precipitation by the end of the 21<sup>st</sup>century relative to the recent past (1995-2014) in the 23 CMIP6 models is over 50% (20%) larger than that in corresponding scenarios of the 40 CMIP5 models. The estimated ranges of precipitation sensitivity in four Tier-1 SSPs are also larger than those in corresponding CMIP5 RCPs. The large increase in projected precipitation change in the highest quartile over ocean is mainly due to the increased number of high equilibrium climate sensitivity (ECS) models in CMIP6 compared to CMIP5, but not over land due to different response of thermodynamic moisture convergence and dynamic processes to global warming. We further discuss key challenges in constraining future precipitation change and source of uncertainties in land precipitation change.</span></p>


2020 ◽  
Author(s):  
Baijun Tian

<p>The double-Intertropical Convergence Zone (ITCZ) bias is one of the most outstanding problems in climate models. This study seeks to examine the double-ITCZ bias in the latest state-of-the-art fully coupled global climate models that participated in Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6) in comparison to their previous generations (CMIP3 and CMIP5 models). To that end, we have analyzed the long-term annual mean tropical precipitation distributions and several precipitation bias indices that quantify the double-ITCZ biases in 75 climate models including 24 CMIP3 models, 25 CMIP3 models, and 26 CMIP6 models. We find that the double-ITCZ bias and its big inter-model spread persist in CMIP6 models but the double-ITCZ bias is slightly reduced from CMIP3 or CMIP5 models to CMIP6 models.</p>


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