FIG 5 - uploaded by Huei-Ping Huang
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As in Fig. 2, but for the seasonal mean evaporation (left) climatology and (right) trend for the southwestern United States. The two filled stars are the seasonal climatology deduced from NCEP R2 and ERA-Interim.
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The twentieth-century climatology and twenty-first-century trend in precipitation P, evaporation E, and P - E for selected semiarid U.S. Southwest and Mediterranean regions are compared between ensembles from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5). The twentieth-century simulations are validated with precipita...
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Probabilistic forecasts of weekly and week 3–4 averages of precipitation are constructed using extended logistic regression (ELR) applied to three models (ECMWF, NCEP, and CMA) from the Subseasonal-to- Seasonal (S2S) project. Individual and multimodel ensemble (MME) forecasts are verified over the common period 1999–2010. The regression parameters...
Widespread multiday convective bursts in the southwestern United States during the North American monsoon are often triggered by Gulf of California moisture surges (GoC surges). However, how GoC surges, and the amount and intensity of associated precipitation, will change in response to CO_2-induced warming remains little known, not least because t...
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... Koutroulis et al. (2016) found high accuracy performance of GCMs in climate projection. The Coupled Model Intercomparison Project (CMIP) was established by the World Climate Research Programme to make easier comparisons across different models (Baker & Huang, 2014;Eyring et al., 2016). The added value of CMIP6 models, comparing to the previous version of CMIP models, can be found in (a) including socio-economic pathways in CMIP6 scenarios, (b) acting in coordination with CMIP5 scenarios premises (O'Neill et al., 2014), and (c) CMIP6 updates due to its focusing on biases, processes, and feeds of climate models for the development and support of the inter-comparison model (Heinze et al., 2019). ...
Based on surface air temperature and precipitation, the current study examines the climate fluctuations over Sistan-and-Baluchestan Province, Iran’s second-largest province. This area suffers from insufficient direct observations and a lack of climatic investigation. Three datasets were utilized including in situ data, gridded data (1984–2013), outputs of historical runs (during 1984–2013), and projections under the SSP5-8.5, SSP3-7.0, and SSP1-2.6 scenarios (in 2020–2049) of twenty-six Global Climate Models (GCMs) from the latest Coupled Model Intercomparison Project (CMIP6). The models’ performance has been evaluated and ranked using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in Multi-Criteria Decision Making (MCDM) technique including eight metrics in both seasonal and annual scales. The surface air temperature showed an increasing trend in seasonal and annual scales during 1984–2013, while the monthly precipitation trend increased for September-October-November and decreased for the other seasons and annual scale during 1984–2013. The top-ranked models for simulating surface air temperature (precipitation) were CESM2 (GFDL-ESM4), IPSL-CM6A-LR (UKESM1-0-LL), ACCESS-CM2 (GFDL-ESM4), and MIROC-ES2L (MPI-ESM1-2-LR) models in DJF, MAM, JJA, and SON seasons, respectively, while ACCESS-CM2 (CNRM-CM6-1-HR) model outperformed others in annual scale. Bias-corrected outputs of the top-ranked CMIP6 GCMs showed an increasing trend for surface air temperature in all seasons (from a 0.7 K increase in December-January-February season under SSP3-7.0 scenario to a 2.5 K increase in June-July-August season under SSP5-8.5 scenario) for period 2020–2049, comparing with that in 1984–2013 period. Bias-corrected monthly precipitation projected by top-ranked CMIP6 GCMs indicated both increasing and decreasing trends depending on selected season and scenario. This varied from a 5 mm/month decrease within December-January-February season under SSP5-8.5 scenario to a 13 mm/month increase during the March-April-May season under SSP1-2.6 scenario in 2020-2050, comparing with that from previous years.
... One of the main tools to study extreme weather events is the numerical models including general/regional climate models (GCMs/RCMs). Indeed, various outputs of the GCMs from the recent phases of the Coupled Model Intercomparison Project (CMIP) are valuable sources for investigating weather and climate patterns in the 21st century (Baker and Huang 2014;Heinze et al. 2019). The global and general simulation of the atmosphere-oceanland circulation in the GCMs contains high complexity and uncertainties, and thus RCMs are essential for the microscale simulation of the hydroclimatological variables (Mohan et al. 2018;Patel et al. 2019;Mahala et al. 2021). ...
Climate and extreme hydrometeorological studies are required to reduce risk and vulnerabilities. This study uses different cumulus and microphysics schemes in Weather Research and Forecasting (WRF) model to simulate heavy rainfall events and Cyclone Sidr in Bangladesh, where many extreme events occur. Results show that WRF can capture the cyclone track, intensity, and landfall position. In addition, regionalization and an ensemble method through Bayesian regression model (BRM) are used to improve WRF rainfall simulations. Although regionalization can improve results of the experiments with different schemes, BRM leads to the best performance. To consider uncertainties and evaluate hazards, a probabilistic framework and proper indices based on distributions are used. Spatiotemporal precipitation distributions are used to develop the flash flood index (FFI) to compare the intensity of heavy rainfall events. Results show a large FFI over the central and southeast divisions that indicates the areas prone to the flash flood hazards and high-level risk. We use standardized precipitation index (SPI) in 6- and 12-month time scales based on monthly precipitation of ACCESS-CM2 in 2015–2100 under two scenarios (SSP-126 and 585) to evaluate long-term precipitation changes and drought/flood tendency. In addition, the probability distributions of the precipitation and wind speed are used in reliability analysis of the infrastructure. Target reliability indices determine the proper design rainfall (105 mm d⁻¹) and wind speed (60 m s⁻¹) that leads to a safe design of infrastructure. This study provides an integrated analysis of extreme hydrometeorological events, which is crucial for sustainable adaptation and mitigation plans.
... The historical decades from each WRF/GCM scenario have similar magnitudes for average annual precipitation (somewhat by design since both GCMs are biased adjusted against ERA-Interim data), but the percent differences by end-century largely diverge. WRF/ CCSM and WRF/NorESM exhibit similar spatial patterns of precipitation change, with the Pacific Northwest getting wetter and the desert southwest getting drier, which agree with previous projection studies (Gutzler and Robbins 2011;Kumar et al., 2013;Baker and Huang 2014). The dipole pattern, however, is shifted further south in the WRF/ NorESM simulations, with projected precipitation increases as far south as northern Utah and Colorado. ...
Cool season precipitation plays a critical role in regional water resource management in the western United States. Throughout the twenty-first century, regional precipitation will be impacted by rising temperatures and changing circulation patterns. Changes to precipitation magnitude remain challenging to project; however, precipitation phase is largely dependent on temperature, and temperature predictions from global climate models are generally in agreement. To understand the implications of this dependence, we investigate projected patterns in changing precipitation phase for mountain areas of the western United States over the twenty-first century and how shifts from snow to rain may impact runoff. We downscale two bias-corrected global climate models for historical and end-century decades with the Weather Research and Forecasting (WRF) regional climate model to estimate precipitation phase and spatial patterns at high spatial resolution (9 km). For future decades, we use the RCP 8.5 scenario, which may be considered a very high baseline emissions scenario to quantify snow season differences over major mountain chains in the western U.S. Under this scenario, the average annual snowfall fraction over the Sierra Nevada decreases by >45% by the end of the century. In contrast, for the colder Rocky Mountains, the snowfall fraction decreases by 29%. Streamflow peaks in basins draining the Sierra Nevada are projected to arrive nearly a month earlier by the end of the century. By coupling WRF with a water resources model, we estimate that California reservoirs will shift towards earlier maximum storage by 1–2 months, suggesting that water management strategies will need to adapt to changes in streamflow magnitude and timing.
... Statistical tests were employed to estimate the similarity between the seasonal variability of CMIPs and ERA5 rainfall, T mx and T mn , following Baker and Huang (2014). ...
The objective of this research was to assess the difference in historical simulations and future projections of rainfall and temperature of CMIP5 (RCP4.5 and 8.5) and CMIP6 (SSP2–4.5 and 5–8.5) models over Southeast Asia (SEA). Monthly historical rainfall and temperature estimations of 13 global climate models common to both CMIPs were evaluated to assess their capability to reproduce the spatial distribution and seasonality of European Reanalysis (ERA) rainfall and temperature. Models were used to determine uncertainty with spatiotemporal variability of rainfall and temperature projections. The CMIP6 GCMs did not appear to perform better than the older CMIP5 in SEA unlike other parts of the globe, except for rainfall. The CMIP6 models showed Kling-Gupta Efficiency (KGE) values in the range of −0.48-0.6, 0.21–0.85 and 0.66–0.91 in simulating historical rainfall, maximum temperature and minimum temperature compared to 0.13–0.46, 0.3–0.86 and 0.42–0.92 for CMIP5. The improvement in CMIP6 models in SEA was in the low uncertainty in ensemble simulation. The projections of CMIP5 and CMIP6 showed a relatively smaller increase in temperature with the CMIP6 ensemble when compared to CMIP5 models, while rainfall appeared to decrease. The geographical distribution of the changes indicated a greater increase in temperature in the cooler region than in the warmer region. In contrast, there was increase in rainfall in the wetter region and a smaller improvement in the drier region. This indicates increased homogeneity in temperature spatial variability, but more heterogeneity in rainfall, in the SEA region under climate warming scenarios.
... However, it is expected that the selected GCM would be able to replicate the mean, spatial variability, and distribution of historical climate . It is also suggested that the selection of GCMs based on their performance in simulating both rainfall and temperature as both are equally required for most of the climate change studies (Ahmed et al. 2019a;Nashwan and Shahid 2020;Shiru et al. 2020) GCM simulations disseminated through different phases of coupled model intercomparison project (CMIP) are vital sources for quantitative climate projection over the twentyfirst century (Baker and Huang 2014;Eyring et al. 2016). The CMIP phase 3 (CMIP3) GCM simulations (Meehl et al. 2007) were used to prepare the fourth assessment report of IPCC (Solomon et al. 2007). ...
The relative performance of global climate models (GCMs) of phases 5 and 6 of the coupled model intercomparison project (CMIP5 and CMIP6, respectively) was assessed in this study based on their ability to simulate annual and seasonal mean rainfall and temperature over Bangladesh for the period 1977–2005. Multiple statistical metrics were used to measure the performance of the GCMs at 30 meteorological observation stations. Two robust multi-criteria decision analysis methods were used to integrate the results obtained using different metrics for an unbiased ranking of the GCMs. The results revealed MIROC5 as the most skillful among CMIP5 GCMs and ACCESS-CM2 among CMIP6 GCMs. Overall, CMIP6 MME showed a significant improvement in simulating rainfall and temperature over Bangladesh compared to CMIP5 MME. The highest improvements were found in simulating cold season (winter and post-monsoon) rainfall and temperature in higher elevated areas. The improvement was relatively more for rainfall than for temperature. The models could capture the interannual variability of annual and seasonal rainfall and temperature reliably, except for the winter rainfall. However, systematic wet and cold/warm biases still exist in CMIP6 models for Bangladesh. CMIP6 GCMs showed higher spatial correlations with observed data, but the higher difference in standard deviations and centered root mean square errors compared to CMIP5 GCMs indicates better performance in simulating geographical distribution but lower performance in simulating spatial variability of most of the climate variables except for minimum temperature at different timescales. In terms of Taylor skill score, the CMIP6 MME showed higher performance in simulating rainfall but lower performance in simulating temperature than CMIP5 MME for most of the timeframes. The findings of this study suggest that the added value of rainfall and temperature simulations in CMIP6 models is not consistent among the climate models used in this research. However, it sets a precedent for future research on climate change risk assessment for the scientific community.
... La predicción del clima estacional se puede realizar mediante la utilización de modelos globales o regionales. Los modelos climáticos regionales permiten lograr predicciones más precisas que los modelos globales, ya que tienen una mejor resolución horizontal y parametrizaciones para determinar procesos atmosféricos a menor escala (Reboita et al., 2018), estas simulaciones del clima tienen como función lograr predicciones climáticas en un determinado año o tiempo, como en su comportamiento, mayor o menor (Baker & Huang, 2014). ...
This document presents the results of an analysis on the comparison of the results of a climate simulation model,
regional reanalysis data and local data on precipitation and seasonal temperature from twenty-three meteorological
stations in Guatemala, to detect signs of the ability of the model to reproduce the seasonal climate over a
period of 3 years (1998-2000). The simulation was performed with a regional climate model (RCM), for its dynamic
scale reduction, the boundary conditions were obtained from the ERA-Interim reanalysis data. The model used was
RegCM, version 4, and it was compared with the precipitation and temperature data from the CRU Database at the
Central American regional level and at the national level with three institutions that generate global data (CRU,
TRMM and GPCP) and local data. The convective schemes used were the scheme of Grell on land and Emanuel on
the ocean, with 50 km of spatial resolution. The adjustments made to the settings generated good performance at the
Central American regional level and at the Guatemala level, despite losing skill in some regions and months. The
model adequately reproduces the behavior of seasonal precipitation in most of the rainy season. It underestimates the
temperature at the regional level but at the Guatemala level, it shows a good fit. The comparison with the observed
local data shows that the model fits the period under study, but it is necessary to carry out more experiments with
different spatial and temporal resolutions and to evaluate the persistence of the model.
... However, it is expected that the selected GCM will be able to replicate the mean, spatial variability, and distribution of historical climate (Ahmed et al. 2020). It is also suggested that the selection of GCMs based on their performance in simulating both rainfall and temperature as both are equally required for most of the climate change studies (Ahmed et al. 2019a;Nashwan and Shahid 2020;Shiru et al. 2020) GCM simulations disseminated through different phases of coupled model intercomparison project (CMIP) are vital sources for quantitative climate projection over the twenty-first century (Baker and Huang 2014;Eyring et al. 2016). The CMIP phase 3 (CMIP3) GCM simulations (Meehl et al. 2007) were used to prepare the fourth assessment report of IPCC (Solomon, S. et al. 2007). ...
The relative performance of global climate models (GCMs) of phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively) was assessed in this study based on their ability to simulate annual and seasonal mean rainfall and temperature over Bangladesh for the period 1977–2005. The multiple statistical metrics were used to measure the performance of the GCMs at 30 meteorological observation stations. Two robust multi-criteria decision analysis methods were used to integrate the results obtained using different metrics for an unbiased ranking of the GCMs. The results revealed MIROC5 as the most skilful among CMIP5 GCMs and ACCESS-CM2 among CMIP6 GCMs. Overall, a significant improvement in CMIP6 MME compared to CMIP5 MME was noticed in simulating rainfall over Bangladesh at annual and seasonal scales. CMIP6 MME also showed significant reduction in maximum and minimum temperature biases over Bangladesh. However, systematic wet and cold biases still exist in CMIP6 models for Bangladesh. CMIP6 GCMs showed higher spatial correlation with observed data compared to CMIP5 GCMs, but higher difference in terms of standard deviations and centered root mean square errors, indicating better performance in simulating geographical distribution but lower performance in simulating spatial variability of most of the climate variables for different timescales. In terms of Taylor skill score, the CMIP6 MME showed higher performance in simulating rainfall but lower performance in simulating temperature compared to CMIP5 MME for most of the timeframes. The findings of this study suggest that the added value of rainfall and temperature simulations in CMIP6 models is incompatible with the climate models used in this research.
... A good source for quantitative prediction of climate in the twenty-first century is the simulation of general circulation models (GCMs) collected by the coupled model intercomparison project (CMIP) (Eyring et al. 2016;Baker and Huang 2014). Given the global concerns over the impacts of climate change on water resources and hydrology, it is imperative to model precipitation variations (Arnell 2004;Duan and Mei 2014). ...
The present study aimed to assess the performance of CMIP6 and CMIP5 projects in projecting mean precipitation at annual, summer, autumn, winter, and spring timescales in the north and northeast of Iran over the period 1987-2005 using relative bias, correlation coefficient, root mean square error, relative error, and the Taylor diagram. This is the first attempt to compare CMIP6 and CMIP5 data in an arid region at a seasonal and annual scale. The results showed that the precipitations simulated by the ensembles of CMIP6 and CMIP5 models were different. The relative bias for winter was lower at all stations in CMIP6 than in CMIP5, so CMIP6 performed better in this respect. CMIP6 outperformed CMIP5 in projecting annual and spring precipitation in 60 and 69% of the stations, respectively. Whereas CMIP6 overestimated precipitation in 70% of the stations, CMIP5 underestimated it in 77% of the stations. CMIP5 models exhibited better performance in 70% of the stations only in autumn. In most seasons and stations, CMIP6 CGMs' ensemble outperformed CMIP5. The results of HadGEM2-ES from CMIP5 and CESM2 from CMIP6 were more accurate than the models' ensembles in both projects. Overall, CMIP6 models exhibited better performance than CMIP5 models.
... are projected in the desert southwest and toward the Great Plains of the United States. The spatial pattern of precipitation changes is similar to that reported in other studies (Gutzler and Robbins, 2011;Kumar et al., 2013;Baker and Huang, 2014). Increased precipitation in the Pacific Northwest and decreased precipitation in the southwest United States is consistent with a projected poleward shift of midlatitude storm tracks (Yin, 2005;Salathé, 2006;Mbengue and Schneider, 2013). ...
Extreme precipitation and runoff events, which often impact natural and social systems more than mean changes, generally occur over regional scales. Future climate projections can be used to estimate how the hydrologic cycle may change, but the coarse resolution of global climate models (GCMs) (>1°) makes it difficult to evaluate regional changes, such as over a single watershed. To estimate changes in hydroclimatic variables at finer spatial resolutions, we dynamically downscale the Community Climate System Model version 4 (CCSM4) with the Weather Research and Forecasting (WRF) regional climate model over the western United States at 9 km spatial resolution. By running WRF at a higher spatial resolution, we estimate future climate conditions, including 99% event magnitude, over 17 watersheds: the Columbia, Lower Colorado, Upper Colorado, the Upper Missouri/Yellowstone, and 12 basins draining the western slope of the Sierra Nevada in California. Over each basin, we compare a historical period (1996–2005) with mid-century (2041–2050) and end-century (2091–2100). From the WRF/CCSM simulations, most basins are projected to have earlier peaks in springtime streamflow. The Columbia and the Lower Colorado watersheds are both expected to experience more extreme wet days, with the 99th percentile of daily precipitation estimated to increase by over 10%. For the Upper Colorado, however, the 99th percentile of daily runoff is projected to decrease by over 30%. Basins in the northern and central Sierra Nevada are projected to have substantial increases in extreme runoff, with doubling of high flow event magnitude possible for some basins. By end-century, the contribution of high-magnitude runoff (>90th percentile) to total runoff is projected to increase from 46 to 56%, when averaged across all 12 Sierra Nevada basins. Though only one realization from a single GCM, the downscaled simulation presented here shows interesting results regarding how extreme events may change; these results can be tested by downscaling other global models with WRF to create an ensemble of dynamically downscaled future projections.
... Both CMIP3 and CMIP5 indicate future warming in summer months beyond historic variation, though CMIP5 scenarios cover a greater range of future greenhouse gas concentrations that explain some differences in temperature and precipitation outcomes such as temperature extremes. Both sets of projections are similar in seasonal cycles of precipitation to suggest that the simulated trend is robust (Baker and Huang 2014). We acknowledge the breadth of GCM outputs now available with CMIP5, though we maintain that CMIP3 remains valid for the Southwest given similarities between CMIP3 and CMIP5 projections along with our model validation results. ...
Land managers require information about the ongoing and potential effects of future climate to coordinate responses for ecosystems, species, and human communities at scales that are operationally meaningful. Our study focused on the vulnerability for all upland ecosystem types of Arizona and New Mexico in the southwestern United States. Local vulnerability across the two‐state area was represented by the level of departure for late 21st‐century climate from the characteristic pre‐1990 climate envelope of the ecosystem type at each given location, resulting in a probability surface of climate impacts for the two‐state area and an uncertainty assessment based on agreement in results among multiple global climate models. Though the results varied from one ecosystem type to the next, the majority of lands were forecast as high vulnerability and low uncertainty, reflecting significant agreement among climate model projections for the southwestern United States. We then tested our results in relation to ongoing ecological processes that have both regional and global change implications and discovered significant relationships with wildfire severity, upward tree species recruitment, and the encroachment of scrub into semidesert grassland. The testing helped determine the efficacy of the vulnerability surface, as a product of relatively high spatial and thematic resolution, in supporting local planning and management decisions. Most important, this study links climate and changes in vegetation by ecosystem processes that are already ongoing. The results affirm the value of climate model downscaling and show that this portable approach to correlative modeling has value in determining the location and magnitude of potential climate‐related impacts.
